Input: Recognition of proper nouns in Japanese text has been studied as a part of the more general problem of morphological analysis in Japanese text processing -LRB- -LSB- 1 -RSB- -LSB- 2 -RSB- -RRB-. It has also been studied in the framework of Japanese information extraction -LRB- -LSB- 3 -RSB- -RRB- in recent years. Our approach to the Multi-lingual Evaluation Task -LRB- MET -RRB- for Japanese text is to consider the given task as a morphological analysis problem in Japanese. Our morphological analyzer has done all the necessary work for the recognition and classification of proper names, numerical and temporal expressions, i.e. Named Entity -LRB- NE -RRB- items in the Japanese text. The analyzer is called ``Amorph''. Amorph recognizes NE items in two stages: dictionary lookup and rule application. First, it uses several kinds of dictionaries to segment and tag Japanese character strings. Second, based on the information resulting from the dictionary lookup stage, a set of rules is applied to the segmented strings in order to identify NE items. When a segment is found to be an NE item, this information is added to the segment and it is used to generate the final output.

Recognition of proper nouns

morphological analysis

Japanese text processing

Japanese information extraction

Multi-lingual Evaluation Task -LRB- MET -RRB-

morphological analysis problem

recognition and classification of proper names, numerical and temporal expressions

proper nouns

proper names, numerical and temporal expressions

Named Entity -LRB- NE -RRB- items

NE items

dictionaries

Japanese character strings

rules

NE items

NE item

Japanese text

Japanese text

Japanese

Japanese text

It

approach

task

analyzer

it

morphological analyzer

``Amorph''

Amorph

dictionary lookup

rule application

dictionary lookup stage

Recognition of proper nouns

morphological analysis

proper nouns

Japanese text

Named Entity -LRB- NE -RRB- items

Japanese text

dictionary lookup

Amorph

rule application

Amorph

morphological analysis

Japanese text processing

Japanese information extraction

It

approach

Multi-lingual Evaluation Task -LRB- MET -RRB-

Multi-lingual Evaluation Task -LRB- MET -RRB-

Japanese text

morphological analysis problem

task

Japanese

morphological analysis problem

morphological analyzer

recognition and classification of proper names, numerical and temporal expressions

Amorph

NE items

dictionaries

it

dictionaries

Japanese character strings

dictionary lookup stage

rules

rules

NE items

Named Entity -LRB- NE -RRB- items

proper names, numerical and temporal expressions

dictionary lookup

rule application


Input: We propose to incorporate a priori geometric constraints in a 3 -- D stereo reconstruction scheme to cope with the many cases where image information alone is not sufficient to accurately recover 3 -- D shape. Our approach is based on the iterative deformation of a 3 -- D surface mesh to minimize an objective function. We show that combining anisotropic meshing with a non-quadratic approach to regularization enables us to obtain satisfactory reconstruction results using triangulations with few vertices. Structural or numerical constraints can then be added locally to the reconstruction process through a constrained optimization scheme. They improve the reconstruction results and enforce their consistency with a priori knowledge about object shape. The strong description and modeling properties of differential features make them useful tools that can be efficiently used as constraints for 3 -- D reconstruction.

priori geometric constraints

image information

3 -- D shape

objective function

regularization

triangulations

vertices

Structural or numerical constraints

priori knowledge

object shape

modeling properties

differential features

3 -- D stereo reconstruction scheme

iterative deformation of a 3 -- D surface mesh

anisotropic meshing

non-quadratic approach

reconstruction process

constrained optimization scheme

approach

They

them

reconstruction

reconstruction

3 -- D reconstruction

priori geometric constraints

3 -- D stereo reconstruction scheme

image information

3 -- D shape

iterative deformation of a 3 -- D surface mesh

approach

iterative deformation of a 3 -- D surface mesh

objective function

anisotropic meshing

reconstruction

non-quadratic approach

regularization

non-quadratic approach

reconstruction

triangulations

reconstruction

Structural or numerical constraints

reconstruction process

constrained optimization scheme

Structural or numerical constraints

They

reconstruction

them

3 -- D reconstruction

anisotropic meshing

non-quadratic approach

object shape

priori knowledge


Input: This work proposes a new research direction to address the lack of structures in traditional n-gram models. It is based on a weakly supervised dependency parser that can model speech syntax without relying on any annotated training corpus. Labeled data is replaced by a few hand-crafted rules that encode basic syntactic knowledge. Bayesian inference then samples the rules, disambiguating and combining them to create complex tree structures that maximize a discriminative model's posterior on a target unlabeled corpus. This posterior encodes sparse se-lectional preferences between a head word and its dependents. The model is evaluated on English and Czech newspaper texts, and is then validated on French broadcast news transcriptions.

lack of structures

speech syntax

Labeled data

hand-crafted rules

syntactic knowledge

rules

complex tree structures

discriminative model's posterior

sparse se-lectional preferences

lack of structures in traditional n-gram models

weakly supervised dependency parser

n-gram models

Bayesian inference

annotated training corpus

unlabeled corpus

English and Czech newspaper texts

French broadcast news transcriptions

them

posterior

model

weakly supervised dependency parser

speech syntax

hand-crafted rules

syntactic knowledge

Bayesian inference

rules

them

complex tree structures

complex tree structures

discriminative model's posterior

unlabeled corpus

discriminative model's posterior

posterior

sparse se-lectional preferences

English and Czech newspaper texts

model

French broadcast news transcriptions

model


Input: Listen-Communicate-Show -LRB- LCS -RRB- is a new paradigm for human interaction with data sources. We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources. We have built and will demonstrate an application of this approach called LCS-Marine. Using LCS-Marine, tactical personnel can converse with their logistics system to place a supply or information request. The request is passed to a mobile, intelligent agent for execution at the appropriate database. Requestors can also instruct the system to notify them when the status of a request changes or when a request is complete. We have demonstrated this capability in several field exercises with the Marines and are currently developing applications of this technology in new domains.

Listen-Communicate-Show -LRB- LCS -RRB-

human interaction with data sources

LCS-Marine

LCS-Marine

spoken language understanding system

intelligent mobile agents

mobile, intelligent agent

information sources

new domains

approach

system

Listen-Communicate-Show -LRB- LCS -RRB-

human interaction with data sources

approach

LCS-Marine

intelligent mobile agents

spoken language understanding system


Input: A domain independent model is proposed for the automated interpretation of nominal compounds in English. This model is meant to account for productive rules of interpretation which are inferred from the morpho-syntactic and semantic characteristics of the nominal constituents. In particular, we make extensive use of Pustejovsky's principles concerning the predicative information associated with nominals. We argue that it is necessary to draw a line between generalizable semantic principles and domain-specific semantic information. We explain this distinction and we show how this model may be applied to the interpretation of compounds in real texts, provided that complementary semantic information are retrieved.

domain independent model

Pustejovsky's principles

automated interpretation of nominal compounds

interpretation of compounds

nominal compounds

productive rules of interpretation

morpho-syntactic and semantic characteristics

nominal constituents

predicative information

nominals

generalizable semantic principles

domain-specific semantic information

semantic information

English

model

model

domain independent model

automated interpretation of nominal compounds

model

productive rules of interpretation

morpho-syntactic and semantic characteristics

productive rules of interpretation

model

interpretation of compounds

English

nominal compounds

morpho-syntactic and semantic characteristics

nominal constituents

nominals

predicative information

generalizable semantic principles

domain-specific semantic information


Input: We present a new method for detecting interest points using histogram information. Unlike existing interest point detectors, which measure pixel-wise differences in image intensity, our detectors incorporate histogram-based representations, and thus can find image regions that present a distinct distribution in the neighborhood. The proposed detectors are able to capture large-scale structures and distinctive textured patterns, and exhibit strong invariance to rotation, illumination variation, and blur. The experimental results show that the proposed histogram-based interest point detectors perform particularly well for the tasks of matching textured scenes under blur and illumination changes, in terms of repeatability and distinctiveness. An extension of our method to space-time interest point detection for action classification is also presented.

method

detectors

detectors

method

detecting interest points

matching textured scenes

space-time interest point detection

action classification

histogram information

large-scale structures

distinctive textured patterns

rotation

illumination variation

blur

blur and illumination changes

interest point detectors

histogram-based representations

histogram-based interest point detectors

pixel-wise differences in image intensity

repeatability

distinctiveness

method

detecting interest points

histogram information

detecting interest points

detectors

large-scale structures

detectors

distinctive textured patterns

detectors

rotation

detectors

illumination variation

detectors

blur

histogram-based interest point detectors

matching textured scenes

method

space-time interest point detection

space-time interest point detection

action classification

pixel-wise differences in image intensity

interest point detectors

repeatability

histogram-based interest point detectors

distinctiveness

histogram-based interest point detectors

histogram-based representations

detectors

large-scale structures

distinctive textured patterns

rotation

illumination variation

illumination variation

blur

repeatability

distinctiveness


Input: We have implemented a restricted domain parser called Plume. Building on previous work at Carnegie-Mellon University e.g. -LSB- 4, 5, 8 -RSB-, Plume's approach to parsing is based on semantic caseframe instantiation. This has the advantages of efficiency on grammatical input, and robustness in the face of ungrammatical input. While Plume is well adapted to simple declarative and imperative utterances, it handles passives, relative clauses and interrogatives in an ad hoc manner leading to patchy syntactic coverage. This paper outlines Plume as it currently exists and describes our detailed design for extending Plume to handle passives, relative clauses, and interrogatives in a general manner.

restricted domain parser

Plume

Plume's approach

Plume

Plume

Plume

parsing

semantic caseframe instantiation

grammatical input

ungrammatical input

passives

relative clauses

interrogatives

patchy syntactic coverage

passives

relative clauses

interrogatives

robustness

declarative and imperative utterances

it

it

Plume

restricted domain parser

Plume's approach

parsing

semantic caseframe instantiation

Plume's approach

Plume

declarative and imperative utterances

it

passives

it

relative clauses

it

interrogatives

Plume

passives

Plume

relative clauses

Plume

interrogatives

ungrammatical input

robustness

passives

relative clauses

relative clauses

interrogatives

passives

relative clauses

relative clauses

interrogatives


Input: In this paper, we present an unlexicalized parser for German which employs smoothing and suffix analysis to achieve a labelled bracket F-score of 76.2, higher than previously reported results on the NEGRA corpus. In addition to the high accuracy of the model, the use of smoothing in an unlexicalized parser allows us to better examine the interplay between smoothing and parsing results.

unlexicalized parser

smoothing

suffix analysis

smoothing

unlexicalized parser

smoothing

parsing

German

NEGRA corpus

labelled bracket F-score

accuracy

model

unlexicalized parser

German

smoothing

unlexicalized parser

suffix analysis

unlexicalized parser

smoothing

unlexicalized parser

smoothing

suffix analysis

labelled bracket F-score

unlexicalized parser

NEGRA corpus

unlexicalized parser

accuracy

model


Input: This paper presents an unsupervised learning approach to disambiguate various relations between named entities by use of various lexical and syntactic features from the contexts. It works by calculating eigenvectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. Experiment results on ACE corpora show that this spectral clustering based approach outperforms the other clustering methods.

unsupervised learning approach

spectral clustering based approach

clustering methods

relations between named entities

lexical and syntactic features

eigenvectors

adjacency graph's Laplacian

submanifold

high dimensionality space

eigenvectors

It

cluster number estimation

ACE corpora

unsupervised learning approach

relations between named entities

lexical and syntactic features

unsupervised learning approach

It

submanifold

eigenvectors

It

high dimensionality space

submanifold

cluster number estimation

It

cluster number estimation

eigenvectors

adjacency graph's Laplacian

eigenvectors

ACE corpora

spectral clustering based approach

ACE corpora

clustering methods

spectral clustering based approach

clustering methods


Input: This paper proposes a generic mathematical formalism for the combination of various structures: strings, trees, dags, graphs, and products of them. The polarization of the objects of the elementary structures controls the saturation of the final structure. This formalism is both elementary and powerful enough to strongly simulate many grammar formalisms, such as rewriting systems, dependency grammars, TAG, HPSG and LFG.

mathematical formalism

grammar formalisms

rewriting systems

dependency grammars

structures

them

formalism

strings

trees

dags

graphs

polarization

elementary structures

TAG

HPSG

LFG

strings

structures

trees

structures

dags

structures

graphs

structures

rewriting systems

grammar formalisms

dependency grammars

grammar formalisms

TAG

grammar formalisms

HPSG

grammar formalisms

LFG

grammar formalisms

strings

trees

trees

dags

dags

graphs

rewriting systems

dependency grammars

dependency grammars

TAG

TAG

HPSG

HPSG

LFG

formalism

grammar formalisms


Input: A mixed-signal paradigm is presented for high-resolution parallel inner-product computation in very high dimensions, suitable for efficient implementation of kernels in image processing. At the core of the externally digital architecture is a high-density, low-power analog array performing binary-binary partial matrix-vector multiplication. Full digital resolution is maintained even with low-resolution analog-to-digital conversion, owing to random statistics in the analog summation of binary products. A random modulation scheme produces near-Bernoulli statistics even for highly correlated inputs. The approach is validated with real image data, and with experimental results from a CID/DRAM analog array prototype in 0.5 cents m CMOS.

mixed-signal paradigm

kernels

externally digital architecture

binary-binary partial matrix-vector multiplication

low-resolution analog-to-digital conversion

random modulation scheme

high-resolution parallel inner-product computation

image processing

high-density, low-power analog array

random statistics

analog summation of binary products

near-Bernoulli statistics

CID/DRAM analog array prototype

Full digital resolution

highly correlated inputs

real image data

approach

mixed-signal paradigm

high-resolution parallel inner-product computation

mixed-signal paradigm

kernels

kernels

image processing

binary-binary partial matrix-vector multiplication

high-density, low-power analog array

random modulation scheme

near-Bernoulli statistics

highly correlated inputs

random modulation scheme

high-density, low-power analog array

externally digital architecture

random statistics

analog summation of binary products

real image data

approach


Input: In this paper we specialize the projective unifocal, bifo-cal, and trifocal tensors to the affine case, and show how the tensors obtained relate to the registered tensors encountered in previous work. This enables us to obtain an affine specialization of known projective relations connecting points and lines across two or three views. In the simpler case of affine cameras we give neccessary and sufficient constraints on the components of the trifocal tensor, together with a simple geometric interpretation. Finally, we show how the estimation of the tensors from point correspondences is achieved through factorization, and discuss the estimation from line correspondences.

projective unifocal, bifo-cal, and trifocal tensors

affine cameras

trifocal tensor

factorization

affine case

registered tensors

affine specialization

geometric interpretation

point correspondences

line correspondences

tensors

tensors

estimation

estimation of the tensors

projective unifocal, bifo-cal, and trifocal tensors

affine case

point correspondences

estimation of the tensors

factorization

tensors

line correspondences

estimation


Input: This paper presents an algorithm for selecting an appropriate classifier word for a noun. In Thai language, it frequently happens that there is fluctuation in the choice of classifier for a given concrete noun, both from the point of view of the whole speech community and individual speakers. Basically, there is no exact rule for classifier selection. As far as we can do in the rule-based approach is to give a default rule to pick up a corresponding classifier of each noun. Registration of classifier for each noun is limited to the type of unit classifier because other types are open due to the meaning of representation. We propose a corpus-based method -LRB- Biber ,1993; Nagao ,1993; Smadja ,1993 -RRB- which generates Noun Classifier Associations -LRB- NCA -RRB- to overcome the problems in classifier assignment and semantic construction of noun phrase. The NCA is created statistically from a large corpus and recomposed under concept hierarchy constraints and frequency of occurrences.

Thai language

classifier

rule-based approach

classifier

classifier

corpus-based method

Noun Classifier Associations -LRB- NCA -RRB-

NCA

classifier selection

classifier assignment

semantic construction of noun phrase

type of unit classifier

concept hierarchy constraints

frequency of occurrences

corpus-based method

Noun Classifier Associations -LRB- NCA -RRB-

corpus-based method

classifier assignment

corpus-based method

semantic construction of noun phrase

Noun Classifier Associations -LRB- NCA -RRB-

classifier assignment

Noun Classifier Associations -LRB- NCA -RRB-

semantic construction of noun phrase

concept hierarchy constraints

NCA

frequency of occurrences

NCA

classifier assignment

semantic construction of noun phrase


Input: The perception of transparent objects from images is known to be a very hard problem in vision. Given a single image, it is difficult to even detect the presence of transparent objects in the scene. In this paper, we explore what can be said about transparent objects by a moving observer. We show how features that are imaged through a transparent object behave differently from those that are rigidly attached to the scene. We present a novel model-based approach to recover the shapes and the poses of transparent objects from known motion. The objects can be complex in that they may be composed of multiple layers with different refractive indices. We have conducted numerous simulations to verify the practical feasibility of our algorithm. We have applied it to real scenes that include transparent objects and recovered the shapes of the objects with high accuracy.

perception of transparent objects

images

real scenes

transparent objects

transparent objects

features

transparent object

shapes and the poses of transparent objects

known motion

multiple layers

refractive indices

transparent objects

shapes of the objects

those

objects

they

algorithm

it

model-based approach

accuracy

images

perception of transparent objects

model-based approach

shapes and the poses of transparent objects

known motion

shapes and the poses of transparent objects

it

real scenes

it

shapes of the objects

those

features

multiple layers

they

transparent objects

real scenes

refractive indices

multiple layers

accuracy

shapes of the objects


Input: We propose a novel probabilistic framework for learning visual models of 3D object categories by combining appearance information and geometric constraints. Objects are represented as a coherent ensemble of parts that are consistent under 3D viewpoint transformations. Each part is a collection of salient image features. A generative framework is used for learning a model that captures the relative position of parts within each of the discretized viewpoints. Contrary to most of the existing mixture of viewpoints models, our model establishes explicit correspondences of parts across different viewpoints of the object class. Given a new image, detection and classification are achieved by determining the position and viewpoint of the model that maximize recognition scores of the candidate objects. Our approach is among the first to propose a generative proba-bilistic framework for 3D object categorization. We test our algorithm on the detection task and the viewpoint classification task by using'' car'' category from both the Savarese et al. 2007 and PASCAL VOC 2006 datasets. We show promising results in both the detection and viewpoint classification tasks on these two challenging datasets.

probabilistic framework

generative framework

mixture of viewpoints models

generative proba-bilistic framework

visual models of 3D object categories

detection

classification

3D object categorization

detection task

viewpoint classification task

detection and viewpoint classification tasks

appearance information

geometric constraints

3D viewpoint transformations

salient image features

discretized viewpoints

viewpoints

position

viewpoint

model

model

approach

algorithm

datasets

image

PASCAL VOC 2006 datasets

recognition scores

probabilistic framework

visual models of 3D object categories

appearance information

probabilistic framework

geometric constraints

probabilistic framework

generative framework

model

image

detection

image

classification

position

detection

position

classification

viewpoint

detection

viewpoint

classification

generative proba-bilistic framework

3D object categorization

algorithm

detection task

algorithm

viewpoint classification task

appearance information

geometric constraints

detection

classification

position

viewpoint

detection task

viewpoint classification task

model

mixture of viewpoints models

PASCAL VOC 2006 datasets

algorithm

datasets

detection and viewpoint classification tasks


Input: We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars -LRB- LFG -RRB- to the domain of sentence condensation. Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection. Furthermore, we propose the use of standard parser evaluation methods for automatically evaluating the summarization quality of sentence condensation systems. An experimental evaluation of summarization quality shows a close correlation between the automatic parse-based evaluation and a manual evaluation of generated strings. Overall summarization quality of the proposed system is state-of-the-art, with guaranteed grammaticality of the system output due to the use of a constraint-based parser/generator.

ambiguity packing and stochastic disambiguation techniques

Lexical-Functional Grammars -LRB- LFG -RRB-

linguistic parser/generator

LFG

transfer component

maximum-entropy model

parser evaluation methods

sentence condensation systems

automatic parse-based evaluation

manual evaluation

constraint-based parser/generator

sentence condensation

parse reduction

stochastic output selection

system

system

packed parse forests

summarization quality

summarization quality

summarization quality

grammaticality

ambiguity packing and stochastic disambiguation techniques

Lexical-Functional Grammars -LRB- LFG -RRB-

ambiguity packing and stochastic disambiguation techniques

sentence condensation

linguistic parser/generator

LFG

transfer component

parse reduction

packed parse forests

parse reduction

maximum-entropy model

stochastic output selection

constraint-based parser/generator

system

linguistic parser/generator

system

transfer component

system

maximum-entropy model

system

linguistic parser/generator

transfer component

transfer component

maximum-entropy model

parser evaluation methods

summarization quality

summarization quality

sentence condensation systems

summarization quality

automatic parse-based evaluation

summarization quality

system

grammaticality

system

automatic parse-based evaluation

manual evaluation


Input: The robust principal component analysis -LRB- robust PCA -RRB- problem has been considered in many machine learning applications, where the goal is to decompose the data matrix to a low rank part plus a sparse residual. While current approaches are developed by only considering the low rank plus sparse structure, in many applications, side information of row and/or column entities may also be given, and it is still unclear to what extent could such information help robust PCA. Thus, in this paper, we study the problem of robust PCA with side information, where both prior structure and features of entities are exploited for recovery. We propose a convex problem to incorporate side information in robust PCA and show that the low rank matrix can be exactly recovered via the proposed method under certain conditions. In particular, our guarantee suggests that a substantial amount of low rank matrices, which can not be recovered by standard robust PCA, become re-coverable by our proposed method. The result theoretically justifies the effectiveness of features in robust PCA. In addition, we conduct synthetic experiments as well as a real application on noisy image classification to show that our method also improves the performance in practice by exploiting side information.

robust principal component analysis -LRB- robust PCA -RRB- problem

robust PCA

robust PCA

robust PCA

robust PCA

robust PCA

machine learning applications

recovery

convex problem

noisy image classification

data matrix

low rank part

sparse residual

low rank plus sparse structure

side information

side information

prior structure

features of entities

side information

low rank matrix

low rank matrices

features

side information

approaches

information

method

method

method

robust principal component analysis -LRB- robust PCA -RRB- problem

machine learning applications

low rank plus sparse structure

approaches

information

robust PCA

side information

robust PCA

prior structure

recovery

features of entities

recovery

convex problem

side information

method

low rank matrix

method

low rank matrices

side information

method

low rank part

data matrix

sparse residual

data matrix

side information

robust PCA

low rank part

sparse residual

prior structure

features of entities

features

robust PCA

noisy image classification

method


Input: This paper presents necessary and sufficient conditions for the use of demonstrative expressions in English and discusses implications for current discourse processing algorithms. We examine a broad range of texts to show how the distribution of demonstrative forms and functions is genre dependent. This research is part of a larger study of anaphoric expressions, the results of which will be incorporated into a natural language generation system.

demonstrative expressions

demonstrative forms and functions

anaphoric expressions

English

implications

discourse processing algorithms

natural language generation system

demonstrative expressions

English

implications

discourse processing algorithms

anaphoric expressions

natural language generation system


Input: In the study of expressive speech communication, it is commonly accepted that the emotion perceived by the listener is a good approximation of the intended emotion conveyed by the speaker. This paper analyzes the validity of this assumption by comparing the mismatches between the assessments made by na ¨ ıve listeners and by the speakers that generated the data. The analysis is based on the hypothesis that people are better decoders of their own emotions. Therefore, self-assessments will be closer to the intended emotions. Using the IEMOCAP database, discrete -LRB- categorical -RRB- and continuous -LRB- attribute -RRB- emotional assessments evaluated by the actors and na ¨ ıve listeners are compared. The results indicate that there is a mismatch between the expression and perception of emotion. The speakers in the database assigned their own emotions to more specific emotional categories, which led to more extreme values in the activation-valence space.

expressive speech communication

discrete -LRB- categorical -RRB- and continuous -LRB- attribute -RRB- emotional assessments

IEMOCAP database

expression and perception of emotion

activation-valence space

database

IEMOCAP database

discrete -LRB- categorical -RRB- and continuous -LRB- attribute -RRB- emotional assessments


Input: The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals. The signals are assumed to be piecewise stationary with varying variances in different epochs. In comparison with previous works, in this paper it is assumed that the signals are not i.i.d. in each epoch, but obey a first-order autoregressive model. This model was shown to be more appropriate for blind separation of natural speech signals. A separation method is proposed that is nearly statistically efficient -LRB- approaching the corresponding Cramér-Rao lower bound -RRB-, if the separated signals obey the assumed model. In the case of natural speech signals, the method is shown to have separation accuracy better than the state-of-the-art methods.

blind separation of underdetermined instantaneous mixtures of independent signals

blind separation of natural speech signals.

method

signals

signals

model

assumed model

method

methods

nonstationarity

original signals

Cramér-Rao lower bound -RRB-

natural speech signals

first-order autoregressive model

separation method

separation accuracy

method

blind separation of underdetermined instantaneous mixtures of independent signals

nonstationarity

method

first-order autoregressive model

signals

model

blind separation of natural speech signals.

method

natural speech signals

methods

natural speech signals

Cramér-Rao lower bound -RRB-

separation method

method

methods

separation accuracy

method

separation accuracy

methods


Input: This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extraction. Our study reveals that the syntactic structure features embedded in a parse tree are very effective for relation extraction and these features can be well captured by the convolution tree kernel. Evaluation on the ACE 2003 corpus shows that the convolution kernel over parse trees can achieve comparable performance with the previous best-reported feature-based methods on the 24 ACE relation subtypes. It also shows that our method significantly outperforms the previous two dependency tree kernels on the 5 ACE relation major types.

convolution kernel over parse trees

convolution tree kernel

convolution kernel over parse trees

feature-based methods

dependency tree kernels

syntactic structure information

syntactic structure features

parse tree

relation extraction

relation extraction

features

method

ACE 2003 corpus

convolution kernel over parse trees

syntactic structure information

syntactic structure information

relation extraction

syntactic structure features

relation extraction

convolution tree kernel

features

syntactic structure features

parse tree

ACE 2003 corpus

convolution kernel over parse trees

feature-based methods

convolution kernel over parse trees

method

dependency tree kernels


Input: This paper presents the results of automatically inducing a Combinatory Categorial Grammar -LRB- CCG -RRB- lexicon from a Turkish dependency treebank. The fact that Turkish is an agglutinating free word order language presents a challenge for language theories. We explored possible ways to obtain a compact lexicon, consistent with CCG principles, from a treebank which is an order of magnitude smaller than Penn WSJ.

automatically inducing a Combinatory Categorial Grammar -LRB- CCG -RRB- lexicon

Combinatory Categorial Grammar -LRB- CCG -RRB- lexicon

compact lexicon

Turkish dependency treebank

Turkish

agglutinating free word order language

Penn WSJ

language theories

CCG principles

treebank

Combinatory Categorial Grammar -LRB- CCG -RRB- lexicon

Turkish dependency treebank

compact lexicon

treebank

Turkish

agglutinating free word order language

treebank

Penn WSJ


Input: While sentence extraction as an approach to summarization has been shown to work in documents of certain genres, because of the conversational nature of email communication where utterances are made in relation to one made previously, sentence extraction may not capture the necessary segments of dialogue that would make a summary coherent. In this paper, we present our work on the detection of question-answer pairs in an email conversation for the task of email summarization. We show that various features based on the structure of email-threads can be used to improve upon lexical similarity of discourse segments for question-answer pairing.

sentence extraction

sentence extraction

summarization

detection of question-answer pairs

email summarization

question-answer pairing

email communication

email conversation

features

structure of email-threads

lexical similarity

discourse segments

sentence extraction

summarization

detection of question-answer pairs

email summarization

email conversation

detection of question-answer pairs

features

lexical similarity

features

question-answer pairing

structure of email-threads

features

lexical similarity

discourse segments


Input: In this paper we discuss object detection when only a small number of training examples are given. Specifically, we show how to incorporate a simple prior on the distribution of natural images into support vector machines. SVMs are known to be robust to overfitting; however, a few training examples usually do not represent well the structure of the class. Thus the resulting detectors are not robust and highly depend on the choice of the training examples. We incorporate the prior on natural images by requiring that the separating hyperplane will not only yield a wide margin, but also that the corresponding positive half space will have a low probability to contain natural images -LRB- the background -RRB-. Our experiments on real data sets show that the resulting detector is more robust to the choice of training examples, and substantially improves both linear and kernel SVM when trained on 10 positive and 10 negative examples.

object detection

prior on the distribution of natural images

overfitting

prior on natural images

hyperplane

support vector machines

SVMs

detectors

detector

linear and kernel SVM

real data sets

prior on the distribution of natural images

support vector machines

SVMs

overfitting

real data sets

detector

detector

linear and kernel SVM


Input: Although the study of clustering is centered around an intuitively compelling goal, it has been very difficult to develop a unified framework for reasoning about it at a technical level, and profoundly diverse approaches to clustering abound in the research community. Here we suggest a formal perspective on the difficulty in finding such a unification, in the form of an impossibility theorem: for a set of three simple properties, we show that there is no clustering function satisfying all three. Relaxations of these properties expose some of the interesting -LRB- and unavoidable -RRB- trade-offs at work in well-studied clustering techniques such as single-linkage, sum-of-pairs, k-means, and k-median.

clustering

reasoning

unified framework

unification

impossibility theorem

well-studied clustering techniques

single-linkage

sum-of-pairs

k-means

k-median

clustering function

unified framework

reasoning

single-linkage

well-studied clustering techniques

sum-of-pairs

well-studied clustering techniques

k-means

well-studied clustering techniques

k-median

well-studied clustering techniques

single-linkage

sum-of-pairs

sum-of-pairs

k-means

k-means

k-median


Input: We investigate independent and relevant event-based extractive mutli-document summarization approaches. In this paper, events are defined as event terms and associated event elements. With independent approach, we identify important contents by frequency of events. With relevant approach, we identify important contents by PageRank algorithm on the event map constructed from documents. Experimental results are encouraging.

independent and relevant event-based extractive mutli-document summarization approaches

independent approach

relevant approach

PageRank algorithm

event map

documents

PageRank algorithm

relevant approach

event map

PageRank algorithm

documents

event map


Input: We present a scanning method that recovers dense sub-pixel camera-projector correspondence without requiring any photometric calibration nor preliminary knowledge of their relative geometry. Subpixel accuracy is achieved by considering several zero-crossings defined by the difference between pairs of unstructured patterns. We use gray-level band-pass white noise patterns that increase robustness to indirect lighting and scene discontinuities. Simulated and experimental results show that our method recovers scene geometry with high subpixel precision, and that it can handle many challenges of active reconstruction systems. We compare our results to state of the art methods such as mi-cro phase shifting and modulated phase shifting.

scanning method

mi-cro phase shifting

modulated phase shifting

dense sub-pixel camera-projector correspondence

photometric calibration

relative geometry

zero-crossings

unstructured patterns

gray-level band-pass white noise patterns

indirect lighting

scene discontinuities

scene geometry

Subpixel accuracy

robustness

subpixel precision

method

it

state of the art methods

active reconstruction systems

scanning method

dense sub-pixel camera-projector correspondence

zero-crossings

Subpixel accuracy

method

scene geometry

it

active reconstruction systems

robustness

gray-level band-pass white noise patterns

indirect lighting

robustness

scene discontinuities

robustness

subpixel precision

scene geometry

indirect lighting

scene discontinuities

mi-cro phase shifting

modulated phase shifting

mi-cro phase shifting

state of the art methods

modulated phase shifting

state of the art methods


Input: This paper describes a novel system for acquiring adjectival subcategorization frames -LRB- scfs -RRB- and associated frequency information from English corpus data. The system incorporates a decision-tree classifier for 30 scf types which tests for the presence of grammatical relations -LRB- grs -RRB- in the output of a robust statistical parser. It uses a powerful pattern-matching language to classify grs into frames hierarchically in a way that mirrors inheritance-based lexica. The experiments show that the system is able to detect scf types with 70% precision and 66% recall rate. A new tool for linguistic annotation of scfs in corpus data is also introduced which can considerably alleviate the process of obtaining training and test data for subcategorization acquisition.

system

system

It

system

tool

acquiring adjectival subcategorization frames -LRB- scfs -RRB-

linguistic annotation of scfs

subcategorization acquisition

subcategorization frames -LRB- scfs -RRB-

grammatical relations -LRB- grs -RRB-

pattern-matching language

grs

inheritance-based lexica

scfs

English corpus data

training and test data

decision-tree classifier

robust statistical parser

precision

recall

system

acquiring adjectival subcategorization frames -LRB- scfs -RRB-

decision-tree classifier

grammatical relations -LRB- grs -RRB-

pattern-matching language

It

pattern-matching language

grs

tool

linguistic annotation of scfs

training and test data

subcategorization acquisition

decision-tree classifier

system

precision

system

recall

system

precision

recall


Input: Machine transliteration/back-transliteration plays an important role in many multilingual speech and language applications. In this paper, a novel framework for machine transliteration/backtransliteration that allows us to carry out direct orthographical mapping -LRB- DOM -RRB- between two different languages is presented. Under this framework, a joint source-channel transliteration model, also called n-gram transliteration model -LRB- n-gram TM -RRB-, is further proposed to model the transliteration process. We evaluate the proposed methods through several transliteration/backtransliteration experiments for English/Chinese and English/Japanese language pairs. Our study reveals that the proposed method not only reduces an extensive system development effort but also improves the transliteration accuracy significantly.

Machine transliteration/back-transliteration

multilingual speech and language applications

machine transliteration/backtransliteration

transliteration/backtransliteration

framework

framework

methods

method

direct orthographical mapping -LRB- DOM -RRB-

joint source-channel transliteration model

n-gram transliteration model -LRB- n-gram TM -RRB-

transliteration process

English/Chinese and English/Japanese language pairs

transliteration accuracy

Machine transliteration/back-transliteration

multilingual speech and language applications

framework

machine transliteration/backtransliteration

machine transliteration/backtransliteration

direct orthographical mapping -LRB- DOM -RRB-

framework

joint source-channel transliteration model

n-gram transliteration model -LRB- n-gram TM -RRB-

transliteration process

transliteration/backtransliteration

English/Chinese and English/Japanese language pairs

transliteration/backtransliteration

methods

transliteration accuracy

method


Input: A bio-inspired model for an analog programmable array processor -LRB- APAP -RRB-, based on studies on the vertebrate retina, has permitted the realization of complex programmable spatio-temporal dynamics in VLSI. This model mimics the way in which images are processed in the visual pathway, rendering a feasible alternative for the implementation of early vision applications in standard technologies. A prototype chip has been designed and fabricated in a 0.5 µm standard CMOS process. Computing power per area and power consumption is amongst the highest reported for a single chip. Design challenges, trade-offs and some experimental results are presented in this paper.

bio-inspired model

visual pathway

analog programmable array processor -LRB- APAP -RRB-

VLSI

vision applications

vertebrate retina

complex programmable spatio-temporal dynamics

prototype chip

CMOS process

model

images

Computing power per area

power consumption

bio-inspired model

analog programmable array processor -LRB- APAP -RRB-

bio-inspired model

complex programmable spatio-temporal dynamics

vertebrate retina

bio-inspired model

visual pathway

images

complex programmable spatio-temporal dynamics

VLSI

Computing power per area

power consumption


Input: Determiners play an important role in conveying the meaning of an utterance, but they have often been disregarded, perhaps because it seemed more important to devise methods to grasp the global meaning of a sentence, even if not in a precise way. Another problem with determiners is their inherent ambiguity. In this paper we propose a logical formalism, which, among other things, is suitable for representing determiners without forcing a particular interpretation when their meaning is still not clear.

Determiners

determiners

logical formalism

ambiguity

determiners

ambiguity

determiners

logical formalism

determiners


Input: We investigate the verbal and nonverbal means for grounding, and propose a design for embodied conversational agents that relies on both kinds of signals to establish common ground in human-computer interaction. We analyzed eye gaze, head nods and attentional focus in the context of a direction-giving task. The distribution of nonverbal behaviors differed depending on the type of dialogue move being grounded, and the overall pattern reflected a monitoring of lack of negative feedback. Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to update dialogue state.

verbal and nonverbal means

embodied conversational agents

ECA

grounding

common ground

human-computer interaction

direction-giving task

design

eye gaze

head nods

attentional focus

nonverbal behaviors

dialogue move

negative feedback

verbal and nonverbal grounding acts

dialogue state

verbal and nonverbal means

grounding

design

embodied conversational agents

common ground

human-computer interaction

verbal and nonverbal grounding acts

ECA

verbal and nonverbal grounding acts

dialogue state

eye gaze

head nods

head nods

attentional focus

eye gaze

direction-giving task

head nods

direction-giving task

attentional focus

direction-giving task


Input: Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model -LRB- HMM -RRB- and maximum entropy -LRB- Maxent -RRB- classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a conditional random field -LRB- CRF -RRB- for this task and relate results with this model to our prior work. We evaluate across two corpora -LRB- conversational telephone speech and broadcast news speech -RRB- on both human transcriptions and speech recognition output. In general, our CRF model yields a lower error rate than the HMM and Max-ent models on the NIST sentence boundary detection task in speech, although it is interesting to note that the best results are achieved by three-way voting among the classifiers. This probably occurs because each model has different strengths and weaknesses for modeling the knowledge sources.

Sentence boundary detection

detecting sentence boundaries

speech

textual and prosodic knowledge sources

conversational telephone speech

broadcast news speech

NIST sentence boundary detection task

speech

knowledge sources

speech recognition output

human transcriptions

speech recognition output

it

task

model

corpora

model

hidden Markov model -LRB- HMM -RRB- and maximum entropy -LRB- Maxent -RRB- classifiers

conditional random field -LRB- CRF -RRB-

CRF model

HMM and Max-ent models

three-way voting

classifiers

error rate

Sentence boundary detection

speech recognition output

speech

Sentence boundary detection

hidden Markov model -LRB- HMM -RRB- and maximum entropy -LRB- Maxent -RRB- classifiers

detecting sentence boundaries

textual and prosodic knowledge sources

hidden Markov model -LRB- HMM -RRB- and maximum entropy -LRB- Maxent -RRB- classifiers

conditional random field -LRB- CRF -RRB-

task

classifiers

three-way voting

model

knowledge sources

corpora

human transcriptions

corpora

speech recognition output

error rate

CRF model

error rate

HMM and Max-ent models

NIST sentence boundary detection task

CRF model

NIST sentence boundary detection task

HMM and Max-ent models

conversational telephone speech

corpora

broadcast news speech

corpora

conversational telephone speech

broadcast news speech

human transcriptions

speech recognition output

CRF model

HMM and Max-ent models

speech

NIST sentence boundary detection task


Input: We propose a novel approach to associate objects across multiple PTZ cameras that can be used to perform camera handoff in wide-area surveillance scenarios. While previous approaches relied on geometric, appearance, or correlation-based information for establishing correspondences between static cameras, they each have well-known limitations and are not extendable to wide-area settings with PTZ cameras. In our approach, the slave camera only passively follows the target -LRB- by loose registration with the master -RRB- and bootstraps itself from its own incoming imagery, thus effectively circumventing the problems faced by previous approaches and avoiding the need to perform any model transfer. Towards this goal, we also propose a novel Multiple Instance Learning -LRB- MIL -RRB- formulation for the problem based on the logistic softmax function of covariance-based region features within a MAP estimation framework. We demonstrate our approach with multiple PTZ camera sequences in typical outdoor surveillance settings and show a comparison with state-of-the-art approaches.

approach

approaches

approach

approaches

approach

state-of-the-art approaches

PTZ cameras

geometric, appearance, or correlation-based information

static cameras

wide-area settings

PTZ cameras

slave camera

model transfer

logistic softmax function of covariance-based region features

multiple PTZ camera sequences

outdoor surveillance settings

camera handoff in wide-area surveillance scenarios

Multiple Instance Learning -LRB- MIL -RRB- formulation

MAP estimation framework

approach

camera handoff in wide-area surveillance scenarios

geometric, appearance, or correlation-based information

approaches

logistic softmax function of covariance-based region features

Multiple Instance Learning -LRB- MIL -RRB- formulation

MAP estimation framework

Multiple Instance Learning -LRB- MIL -RRB- formulation

approach

outdoor surveillance settings

multiple PTZ camera sequences

approach

approach

state-of-the-art approaches


Input: This paper solves a specialized regression problem to obtain sampling probabilities for records in databases. The goal is to sample a small set of records over which evaluating aggregate queries can be done both efficiently and accurately. We provide a principled and provable solution for this problem; it is parameterless and requires no data insights. Unlike standard regression problems, the loss is inversely proportional to the regressed-to values. Moreover, a cost zero solution always exists and can only be excluded by hard budget constraints. A unique form of reg-ularization is also needed. We provide an efficient and simple regularized Empirical Risk Minimization -LRB- ERM -RRB- algorithm along with a theoretical generalization result. Our extensive experimental results significantly improve over both uniform sampling and standard stratified sampling which are de-facto the industry standards.

specialized regression problem

regression problems

sampling probabilities

aggregate queries

loss

regressed-to values

hard budget constraints

reg-ularization

records

databases

records

principled and provable solution

cost zero solution

regularized Empirical Risk Minimization -LRB- ERM -RRB- algorithm

uniform sampling

stratified sampling

problem

it

specialized regression problem

sampling probabilities

sampling probabilities

records

principled and provable solution

problem

hard budget constraints

cost zero solution

records

databases

aggregate queries

records

uniform sampling

stratified sampling


Input: We consider the problem of computing the Kullback-Leibler distance, also called the relative entropy, between a probabilistic context-free grammar and a probabilistic finite automaton. We show that there is a closed-form -LRB- analytical -RRB- solution for one part of the Kullback-Leibler distance, viz the cross-entropy. We discuss several applications of the result to the problem of distributional approximation of probabilistic context-free grammars by means of probabilistic finite automata.

Kullback-Leibler distance

relative entropy

probabilistic context-free grammar

closed-form -LRB- analytical -RRB- solution

Kullback-Leibler distance

cross-entropy

probabilistic context-free grammars

probabilistic finite automata

probabilistic finite automaton

distributional approximation

probabilistic context-free grammar

probabilistic finite automaton

closed-form -LRB- analytical -RRB- solution

Kullback-Leibler distance

closed-form -LRB- analytical -RRB- solution

cross-entropy

probabilistic finite automata

distributional approximation

cross-entropy

Kullback-Leibler distance

distributional approximation

probabilistic context-free grammars


Input: In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: -LRB- i -RRB- we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; -LRB- ii -RRB- we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and -LRB- iii -RRB- we introduce an accurate video sequence'' reillumination'' algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate -LRB- over 99.7% on all three databases -RRB-, significantly out-performing state-of-the-art commercial software and methods from the literature.

illumination

pose invariance

face recognition

image formation

generic face appearance variation

video sequences

face images

video

video sequences

databases

lighting

pose

user motion pattern

extreme illumination changes

smoothness

geodesically local appearance manifold structure

unseen head poses

face motion patterns

illumination

pose

head motion variation

resolution

robustness

recognition rate

photometric model

statistical model

robust same-identity likelihood

video sequence'' reillumination'' algorithm

fully automatic recognition system

commercial software

method

data set

system

methods

illumination

pose invariance

lighting

pose

pose

user motion pattern

photometric model

statistical model

geodesically local appearance manifold structure

robust same-identity likelihood

illumination

pose

pose

head motion variation

commercial software

methods

illumination

face recognition

pose invariance

face recognition

face motion patterns

video

resolution

face images

smoothness

geodesically local appearance manifold structure

face motion patterns

robustness

illumination

video sequences

pose

video sequences

head motion variation

video sequences

photometric model

image formation

statistical model

generic face appearance variation

statistical model

extreme illumination changes

method

fully automatic recognition system

robustness

video sequence'' reillumination'' algorithm

video sequences

fully automatic recognition system

data set

system

recognition rate

system

system

commercial software

system

methods


Input: We present Minimum Bayes-Risk -LRB- MBR -RRB- decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. We report the performance of the MBR decoders on a Chinese-to-English translation task. Our results show that MBR decoding can be used to tune statistical MT performance for specific loss functions.

Minimum Bayes-Risk -LRB- MBR -RRB- decoding

statistical approach

MT system

MBR decoders

MBR decoding

statistical MT

statistical machine translation

translation

Chinese-to-English translation task

expected loss

translation errors

loss functions

loss functions

linguistic information

word-to-word alignments

syntactic structure

parse-trees

loss functions

Minimum Bayes-Risk -LRB- MBR -RRB- decoding

statistical machine translation

linguistic information

loss functions

word-to-word alignments

loss functions

syntactic structure

loss functions

MBR decoders

Chinese-to-English translation task

MBR decoding

statistical MT

MBR decoding

loss functions

loss functions

translation

word-to-word alignments

MT system

parse-trees

syntactic structure


Input: This paper presents a critical discussion of the various approaches that have been used in the evaluation of Natural Language systems. We conclude that previous approaches have neglected to evaluate systems in the context of their use, e.g. solving a task requiring data retrieval. This raises questions about the validity of such approaches. In the second half of the paper, we report a laboratory study using the Wizard of Oz technique to identify NL requirements for carrying out this task. We evaluate the demands that task dialogues collected using this technique, place upon a prototype Natural Language system. We identify three important requirements which arose from the task that we gave our subjects: operators specific to the task of database access, complex contextual reference and reference to the structure of the information source. We discuss how these might be satisfied by future Natural Language systems.

approaches

approaches

systems

task

approaches

task

technique

task

task

evaluation of Natural Language systems

data retrieval

database access

Natural Language systems

Wizard of Oz technique

prototype Natural Language system

Natural Language systems

NL requirements

contextual reference

task dialogues

approaches

evaluation of Natural Language systems

systems

task

Wizard of Oz technique

NL requirements

Wizard of Oz technique

task

task dialogues

prototype Natural Language system

technique

task dialogues

approaches

systems

data retrieval

task


Input: We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. Then, we explore whether information about meeting context can aid classifiers' performances. Both classifiers perform the best when conversational context and utterance features are combined with speaker's gaze information. The classifiers show little gain from information about meeting context.

addressee identification in four-participants face-to-face meetings

Bayesian Network

Naive Bayes classifiers

classifiers

classifiers

classifiers

addressee of a dialogue act

gaze

utterance

conversational context features

meeting context

conversational context

utterance features

speaker's gaze information

meeting context

Bayesian Network

addressee identification in four-participants face-to-face meetings

Naive Bayes classifiers

addressee identification in four-participants face-to-face meetings

gaze

addressee of a dialogue act

utterance

addressee of a dialogue act

conversational context features

addressee of a dialogue act

conversational context

classifiers

utterance features

classifiers

speaker's gaze information

classifiers

Naive Bayes classifiers

Bayesian Network

gaze

utterance

utterance

conversational context features

conversational context

utterance features

speaker's gaze information

utterance features


Input: Towards deep analysis of compositional classes of paraphrases, we have examined a class-oriented framework for collecting paraphrase examples, in which sentential paraphrases are collected for each paraphrase class separately by means of automatic candidate generation and manual judgement. Our preliminary experiments on building a paraphrase corpus have so far been producing promising results, which we have evaluated according to cost-efficiency, exhaustiveness, and reliability.

compositional classes of paraphrases

class-oriented framework

automatic candidate generation

manual judgement

paraphrase examples

sentential paraphrases

paraphrase corpus

class-oriented framework

compositional classes of paraphrases

class-oriented framework

paraphrase examples

automatic candidate generation

sentential paraphrases

manual judgement

sentential paraphrases

automatic candidate generation

manual judgement


Input: The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output of machine translation -LRB- MT -RRB- systems. We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems. This, the first experiment in a series of experiments, looks at the intelligibility of MT output. A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. Even more illuminating was the factors on which the assessors made their decisions. We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output. Subjects were given a set of up to six extracts of translated newswire text. Some of the extracts were expert human translations, others were machine translation outputs. The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or a machine translation. Additionally, they were asked to mark the word at which they made this decision. The results of this experiment, along with a preliminary analysis of the factors involved in the decision making process will be presented here.

automated evaluation techniques

machine translation -LRB- MT -RRB- systems

evaluation of human language learners

human language learning process

translation process

machine translation systems

language learning

evaluation techniques

assessors

assessors

non-native language essays

translated newswire text

machine translation output

expert human translations

machine translation outputs

expert human translation

machine translation

automated evaluation techniques

evaluation of human language learners

evaluation techniques

human language learning process

evaluation techniques

translation process

evaluation techniques

machine translation systems

human language learning process

translation process

translation process

machine translation systems

machine translation outputs

expert human translations

language learning

assessors

expert human translation

machine translation


Input: This paper presents a machine learning approach to bare slice disambiguation in dialogue. We extract a set of heuristic principles from a corpus-based sample and formulate them as probabilistic Horn clauses. We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. Both learners perform well, yielding similar success rates of approx 90%. The results show that the features in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features.

machine learning approach

heuristic principles

machine learning algorithms

rule-based learning algorithm

memory-based system

heuristic principles

bare slice disambiguation

dialogue

corpus-based sample

probabilistic Horn clauses

clauses

domain independent features

features

rules

Horn clauses

features

SLIPPER

TiMBL

success rates

machine learning approach

bare slice disambiguation

dialogue

bare slice disambiguation

corpus-based sample

heuristic principles

probabilistic Horn clauses

heuristic principles

features

heuristic principles

SLIPPER

rule-based learning algorithm

TiMBL

memory-based system

rule-based learning algorithm

machine learning algorithms

memory-based system

machine learning algorithms

rule-based learning algorithm

memory-based system


Input: We suggest a new goal and evaluation criterion for word similarity measures. The new criterion -- meaning-entailing substitutability -- fits the needs of semantic-oriented NLP applications and can be evaluated directly -LRB- independent of an application -RRB- at a good level of human agreement. Motivated by this semantic criterion we analyze the empirical quality of distributional word feature vectors and its impact on word similarity results, proposing an objective measure for evaluating feature vector quality. Finally, a novel feature weighting and selection function is presented, which yields superior feature vectors and better word similarity performance.

evaluation criterion

word similarity measures

meaning-entailing substitutability

human agreement

semantic criterion

feature vector quality

criterion

measure

semantic-oriented NLP applications

word similarity

word similarity

distributional word feature vectors

feature vectors

feature weighting and selection function

evaluation criterion

word similarity measures

meaning-entailing substitutability

semantic-oriented NLP applications

distributional word feature vectors

word similarity

feature weighting and selection function

feature vectors

feature weighting and selection function

word similarity

human agreement

meaning-entailing substitutability

semantic criterion

distributional word feature vectors

measure

feature vector quality

feature vectors

word similarity


Input: Reflections in image sequences consist of several layers superimposed over each other. This phenomenon causes many image processing techniques to fail as they assume the presence of only one layer at each examined site e.g. motion estimation and object recognition. This work presents an automated technique for detecting reflections in image sequences by analyzing motion trajectories of feature points. It models reflection as regions containing two different layers moving over each other. We present a strong detector based on combining a set of weak detectors. We use novel priors, generate sparse and dense detection maps and our results show high detection rate with rejection to pathological motion and occlusion.

Reflections

Reflections in image sequences

motion trajectories

feature points

reflection

priors

sparse and dense detection maps

pathological motion

occlusion

image sequences

image processing techniques

motion estimation

object recognition

detecting reflections in image sequences

technique

It

detector

detectors

detection rate

motion estimation

object recognition

pathological motion

occlusion

technique

detecting reflections in image sequences

motion trajectories

technique

It

reflection

detectors

detector

priors

sparse and dense detection maps

feature points

motion trajectories


Input: This paper considers the problem of reconstructing the motion of a 3D articulated tree from 2D point correspondences subject to some temporal prior. Hitherto, smooth motion has been encouraged using a trajectory basis, yielding a hard combinatorial problem with time complexity growing exponentially in the number of frames. Branch and bound strategies have previously attempted to curb this complexity whilst maintaining global optimality. However, they provide no guarantee of being more efficient than exhaustive search. Inspired by recent work which reconstructs general trajectories using compact high-pass filters, we develop a dynamic programming approach which scales linearly in the number of frames, leveraging the intrinsically local nature of filter interactions. Extension to affine projection enables reconstruction without estimating cameras.

reconstructing the motion of a 3D articulated tree

hard combinatorial problem

reconstruction

2D point correspondences

temporal prior

smooth motion

trajectory basis

global optimality

filter interactions

time complexity

Branch and bound strategies

exhaustive search

compact high-pass filters

dynamic programming approach

affine projection

estimating cameras

complexity

they

2D point correspondences

reconstructing the motion of a 3D articulated tree

trajectory basis

smooth motion

Branch and bound strategies

complexity

affine projection

reconstruction

time complexity

hard combinatorial problem

global optimality

Branch and bound strategies

they

exhaustive search


Input: Topical blog post retrieval is the task of ranking blog posts with respect to their relevance for a given topic. To improve topical blog post retrieval we incorporate textual credibility indicators in the retrieval process. We consider two groups of indicators: post level -LRB- determined using information about individual blog posts only -RRB- and blog level -LRB- determined using information from the underlying blogs -RRB-. We describe how to estimate these indicators and how to integrate them into a retrieval approach based on language models. Experiments on the TREC Blog track test set show that both groups of credibility indicators significantly improve retrieval effectiveness; the best performance is achieved when combining them.

Topical blog post retrieval

ranking blog posts

topical blog post retrieval

blog posts

blog posts

blogs

TREC Blog track test set

relevance

retrieval effectiveness

textual credibility indicators

credibility indicators

retrieval process

retrieval approach

language models

indicators

indicators

them

Topical blog post retrieval

ranking blog posts

relevance

blog posts

textual credibility indicators

topical blog post retrieval

language models

them

textual credibility indicators

retrieval process

them

retrieval approach

TREC Blog track test set

credibility indicators

retrieval effectiveness

credibility indicators


Input: We investigate the problem of learning to predict moves in the board game of Go from game records of expert players. In particular, we obtain a probability distribution over legal moves for professional play in a given position. This distribution has numerous applications in computer Go, including serving as an efficient stand-alone Go player. It would also be effective as a move selector and move sorter for game tree search and as a training tool for Go players. Our method has two major components: a -RRB- a pattern extraction scheme for efficiently harvesting patterns of given size and shape from expert game records and b -RRB- a Bayesian learning algorithm -LRB- in two variants -RRB- that learns a distribution over the values of a move given a board position based on the local pattern context. The system is trained on 181,000 expert games and shows excellent prediction performance as indicated by its ability to perfectly predict the moves made by professional Go players in 34% of test positions.

board game of Go

training tool

game records of expert players

expert games

probability distribution

computer Go

stand-alone Go player

Go players

local pattern context

distribution

It

method

system

move selector

move sorter

game tree search

pattern extraction scheme

Bayesian learning algorithm

game records of expert players

board game of Go

distribution

computer Go

It

move selector

It

move sorter

It

training tool

move selector

game tree search

move sorter

game tree search

training tool

Go players

expert games

system

move selector

move sorter

pattern extraction scheme

Bayesian learning algorithm

pattern extraction scheme

method

Bayesian learning algorithm

method


Input: We present a novel approach for automatically acquiring English topic signatures. Given a particular concept, or word sense, a topic signature is a set of words that tend to co-occur with it. Topic signatures can be useful in a number of Natural Language Processing -LRB- NLP -RRB- applications, such as Word Sense Disambiguation -LRB- WSD -RRB- and Text Summarisation. Our method takes advantage of the different way in which word senses are lexicalised in English and Chinese, and also exploits the large amount of Chinese text available in corpora and on the Web. We evaluated the topic signatures on a WSD task, where we trained a second-order vector cooccurrence algorithm on standard WSD datasets, with promising results.

approach

method

corpora

automatically acquiring English topic signatures

Natural Language Processing -LRB- NLP -RRB- applications

Word Sense Disambiguation -LRB- WSD -RRB-

Text Summarisation

WSD task

concept

word sense

topic signature

Topic signatures

word senses

topic signatures

English

Chinese

Chinese text

Web

WSD datasets

second-order vector cooccurrence algorithm

approach

automatically acquiring English topic signatures

Topic signatures

Natural Language Processing -LRB- NLP -RRB- applications

Topic signatures

Word Sense Disambiguation -LRB- WSD -RRB-

Topic signatures

Text Summarisation

WSD datasets

second-order vector cooccurrence algorithm

Word Sense Disambiguation -LRB- WSD -RRB-

Natural Language Processing -LRB- NLP -RRB- applications

Text Summarisation

Natural Language Processing -LRB- NLP -RRB- applications

Word Sense Disambiguation -LRB- WSD -RRB-

Text Summarisation

corpora

Web

Chinese text

corpora

Chinese text

Web

WSD task

topic signatures


Input: Joint matrix triangularization is often used for estimating the joint eigenstructure of a set M of matrices, with applications in signal processing and machine learning. We consider the problem of approximate joint matrix triangularization when the matrices in M are jointly diagonalizable and real, but we only observe a set M' of noise perturbed versions of the matrices in M. Our main result is a first-order upper bound on the distance between any approximate joint triangularizer of the matrices in M' and any exact joint triangularizer of the matrices in M. The bound depends only on the observable matrices in M' and the noise level. In particular, it does not depend on optimization specific properties of the triangularizer, such as its proximity to critical points, that are typical of existing bounds in the literature. To our knowledge, this is the first a posteriori bound for joint matrix decomposition. We demonstrate the bound on synthetic data for which the ground truth is known.

Joint matrix triangularization

signal processing

machine learning

approximate joint matrix triangularization

joint matrix decomposition

joint eigenstructure

first-order upper bound

posteriori bound

approximate joint triangularizer

exact joint triangularizer

triangularizer

bound

synthetic data

Joint matrix triangularization

joint eigenstructure

joint eigenstructure

signal processing

joint eigenstructure

machine learning

posteriori bound

joint matrix decomposition

signal processing

machine learning

approximate joint triangularizer

exact joint triangularizer


Input: The psycholinguistic literature provides evidence for syntactic priming, i.e., the tendency to repeat structures. This paper describes a method for incorporating priming into an incremental probabilistic parser. Three models are compared, which involve priming of rules between sentences, within sentences, and within coordinate structures. These models simulate the reading time advantage for parallel structures found in human data, and also yield a small increase in overall parsing accuracy.

psycholinguistic literature

syntactic priming

priming

priming

rules

coordinate structures

parallel structures

method

incremental probabilistic parser

human data

parsing accuracy

psycholinguistic literature

syntactic priming

method

priming

priming

incremental probabilistic parser

parallel structures

human data


Input: Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.

Learned confidence measures

stereo algorithm

learned confidence measures

outlier removal

quality improvement

stereo vision

task

problem

approach

approach

them

manual interaction

active sensing devices

synthetic scenes

view points

multiple depth maps

stereo images

KITTI2012 dataset

automatically generated training data

laser ground truth data

Learned confidence measures

outlier removal

Learned confidence measures

quality improvement

manual interaction

task

active sensing devices

task

synthetic scenes

task

view points

approach

approach

learned confidence measures

automatically generated training data

them

outlier removal

quality improvement

manual interaction

active sensing devices

active sensing devices

synthetic scenes

outlier removal

stereo vision

quality improvement

stereo vision

KITTI2012 dataset

learned confidence measures

laser ground truth data

automatically generated training data


Input: An important area of learning in autonomous agents is the ability to learn domain-speciic models of actions to be used by planning systems. In this paper, we present methods by which an agent learns action models from its own experience and from its observation of a domain expert. These methods diier from previous work in the area in two ways: the use of an action model formalism which is better suited to the needs of a re-active agent, and successful implementation of noise-handling mechanisms. Training instances are generated from experience and observation, and a variant of GOLEM is used to learn action models from these instances. The integrated learning system has been experimentally validated in simulated construction and ooce domains.

learning in autonomous agents

planning systems

simulated construction

ooce domains

domain-speciic models of actions

action models

action model formalism

re-active agent

noise-handling mechanisms

GOLEM

action models

integrated learning system

methods

domain expert

methods

learning in autonomous agents

domain-speciic models of actions

planning systems

domain-speciic models of actions

action model formalism

methods

action model formalism

re-active agent

noise-handling mechanisms

methods

GOLEM

action models

simulated construction

integrated learning system

ooce domains

integrated learning system

simulated construction

ooce domains


Input: This paper describes FERRET, an interactive question-answering -LRB- Q/A -RRB- system designed to address the challenges of integrating automatic Q/A applications into real-world environments. FERRET utilizes a novel approach to Q/A known as predictive questioning which attempts to identify the questions -LRB- and answers -RRB- that users need by analyzing how a user interacts with a system while gathering information related to a particular scenario.

FERRET

interactive question-answering -LRB- Q/A -RRB- system

FERRET

Q/A

predictive questioning

integrating automatic Q/A applications into real-world environments

approach

FERRET

interactive question-answering -LRB- Q/A -RRB- system

FERRET

integrating automatic Q/A applications into real-world environments

approach

FERRET

approach

Q/A


Input: In order to build robust automatic abstracting systems, there is a need for better training resources than are currently available. In this paper, we introduce an annotation scheme for scientific articles which can be used to build such a resource in a consistent way. The seven categories of the scheme are based on rhetorical moves of argumentation. Our experimental results show that the scheme is stable, reproducible and intuitive to use.

automatic abstracting systems

training resources

scientific articles

annotation scheme

rhetorical moves of argumentation

resource

scheme

scheme

training resources

automatic abstracting systems

annotation scheme

scientific articles

annotation scheme

resource

rhetorical moves of argumentation

scheme


Input: The automated segmentation of images into semantically meaningful parts requires shape information since low-level feature analysis alone often fails to reach this goal. We introduce a novel method of shape constrained image segmentation which is based on mixtures of feature distributions for color and texture as well as probabilistic shape knowledge. The combined approach is formulated in the framework of Bayesian statistics to account for the robust-ness requirement in image understanding. Experimental evidence shows that semantically meaningful segments are inferred, even when image data alone gives rise to ambiguous segmentations.

automated segmentation

shape constrained image segmentation

robust-ness requirement in image understanding

images

image data

shape information

mixtures of feature distributions

color

texture

probabilistic shape knowledge

low-level feature analysis

method

Bayesian statistics

approach

images

automated segmentation

method

shape constrained image segmentation

mixtures of feature distributions

method

mixtures of feature distributions

color

mixtures of feature distributions

texture

mixtures of feature distributions

probabilistic shape knowledge

approach

robust-ness requirement in image understanding

Bayesian statistics

approach

color

texture

texture

probabilistic shape knowledge


Input: The goal of this work is the enrichment of human-machine interactions in a natural language environment. Because a speaker and listener can not be assured to have the same beliefs, contexts, perceptions, backgrounds, or goals, at each point in a conversation, difficulties and mistakes arise when a listener interprets a speaker's utterance. These mistakes can lead to various kinds of misunderstandings between speaker and listener, including reference failures or failure to understand the speaker's intention. We call these misunderstandings miscommunication. Such mistakes can slow, and possibly break down, communication. Our goal is to recognize and isolate such miscommunications and circumvent them. This paper highlights a particular class of miscommunication -- reference problems -- by describing a case study and techniques for avoiding failures of reference. We want to illustrate a framework less restrictive than earlier ones by allowing a speaker leeway in forming an utterance about a task and in determining the conversational vehicle to deliver it. The paper also promotes a new view for extensional reference.

human-machine interactions

miscommunication

miscommunications

miscommunication

reference problems

failures of reference

extensional reference

natural language environment

reference failures

speaker's intention

them

techniques

natural language environment

human-machine interactions

reference problems

miscommunication

techniques

failures of reference


Input: Combination methods are an effective way of improving system performance. This paper examines the benefits of system combination for unsupervised WSD. We investigate several voting-and arbiter-based combination strategies over a diverse pool of unsupervised WSD systems. Our combination methods rely on predominant senses which are derived automatically from raw text. Experiments using the SemCor and Senseval-3 data sets demonstrate that our ensembles yield significantly better results when compared with state-of-the-art.

Combination methods

system combination

voting-and arbiter-based combination strategies

unsupervised WSD systems

combination methods

unsupervised WSD

predominant senses

raw text

SemCor and Senseval-3 data sets

ensembles

state-of-the-art

system combination

unsupervised WSD

voting-and arbiter-based combination strategies

unsupervised WSD systems

predominant senses

combination methods

raw text

predominant senses

SemCor and Senseval-3 data sets

ensembles

SemCor and Senseval-3 data sets

state-of-the-art


Input: The applicability of many current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. Although hidden Markov models -LRB- HMMs -RRB- provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains. However, one can dramatically improve the quality of the learned structure by exploiting simple prior knowledge of the desired solutions. In both domains, we found that unsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.

information extraction techniques

hidden Markov models -LRB- HMMs -RRB-

generative model

unsupervised HMM learning

unsupervised methods

supervised methods

semi-supervised methods

supervised training data

classified advertisements

bibliographic citations

field structured text

unlabeled examples

labeled examples

labeled data

field structured extraction tasks

prior knowledge

prior knowledge

accuracies

supervised training data

information extraction techniques

prior knowledge

field structured extraction tasks

hidden Markov models -LRB- HMMs -RRB-

generative model

generative model

field structured text

unlabeled examples

unsupervised methods

labeled examples

supervised methods

labeled data

semi-supervised methods

classified advertisements

field structured extraction tasks

bibliographic citations

field structured extraction tasks

classified advertisements

bibliographic citations

unsupervised methods

supervised methods

accuracies

unsupervised methods

accuracies

supervised methods


Input: This paper gives an overall account of a prototype natural language question answering system, called Chat-80. Chat-80 has been designed to be both efficient and easily adaptable to a variety of applications. The system is implemented entirely in Prolog, a programming language based on logic. With the aid of a logic-based grammar formalism called extraposition grammars, Chat-80 translates English questions into the Prolog subset of logic. The resulting logical expression is then transformed by a planning algorithm into efficient Prolog, cf. query optimisation in a relational database. Finally, the Prolog form is executed to yield the answer.

natural language question answering system

logic-based grammar formalism

extraposition grammars

planning algorithm

query optimisation

system

Prolog

programming language

logic

Prolog subset of logic

logical expression

Prolog

Prolog form

relational database

natural language question answering system

Prolog

programming language

extraposition grammars

logic-based grammar formalism

Prolog

system

logic

programming language

extraposition grammars

planning algorithm

logical expression

relational database

query optimisation


Input: Human action recognition from well-segmented 3D skeleton data has been intensively studied and attracting an increasing attention. Online action detection goes one step further and is more challenging, which identifies the action type and localizes the action positions on the fly from the untrimmed stream. In this paper, we study the problem of online action detection from the streaming skeleton data. We propose a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localiza-tion information. By employing a joint classification and regression optimization objective, this network is capable of automatically localizing the start and end points of actions more accurately. Specifically, by leveraging the merits of the deep Long Short-Term Memory -LRB- LSTM -RRB- subnetwork, the proposed model automatically captures the complex long-range temporal dynamics, which naturally avoids the typical sliding window design and thus ensures high computational efficiency. Furthermore, the subtask of regression optimization provides the ability to forecast the action prior to its occurrence. To evaluate our proposed model, we build a large streaming video dataset with annotations. Experimental results on our dataset and the public G3D dataset both demonstrate very promising performance of our scheme.

Human action recognition

Online action detection

online action detection

regression optimization

well-segmented 3D skeleton data

untrimmed stream

streaming skeleton data

streaming video dataset

G3D dataset

action type

action positions

action type

temporal localiza-tion information

joint classification and regression optimization objective

long-range temporal dynamics

computational efficiency

multi-task end-to-end Joint Classification-Regression Recurrent Neural Network

deep Long Short-Term Memory -LRB- LSTM -RRB- subnetwork

sliding window design

network

model

model

dataset

well-segmented 3D skeleton data

Human action recognition

Online action detection

action type

Online action detection

action positions

untrimmed stream

Online action detection

streaming skeleton data

online action detection

multi-task end-to-end Joint Classification-Regression Recurrent Neural Network

action type

multi-task end-to-end Joint Classification-Regression Recurrent Neural Network

temporal localiza-tion information

joint classification and regression optimization objective

network

deep Long Short-Term Memory -LRB- LSTM -RRB- subnetwork

model

action type

action positions

action type

temporal localiza-tion information

dataset

G3D dataset

long-range temporal dynamics

model

streaming video dataset

model


Input: The task of machine translation -LRB- MT -RRB- evaluation is closely related to the task of sentence-level semantic equivalence classification. This paper investigates the utility of applying standard MT evaluation methods -LRB- BLEU, NIST, WER and PER -RRB- to building classifiers to predict semantic equivalence and entailment. We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments.

machine translation -LRB- MT -RRB- evaluation

sentence-level semantic equivalence classification

semantic equivalence

entailment

word matches and non-matches

paraphrase classification

entailment

paraphrase classification

MT evaluation methods

BLEU

NIST

WER

PER

PER

paraphrase classification accuracy

classifiers

classification method

MT evaluation techniques

part of speech information

features

technique

models

machine translation -LRB- MT -RRB- evaluation

sentence-level semantic equivalence classification

BLEU

NIST

NIST

WER

WER

PER

semantic equivalence

entailment

paraphrase classification

entailment

MT evaluation methods

classifiers

classifiers

semantic equivalence

classifiers

entailment

PER

classification method

PER

part of speech information

part of speech information

word matches and non-matches

MT evaluation techniques

features

MT evaluation techniques

paraphrase classification

MT evaluation techniques

entailment

BLEU

MT evaluation methods

NIST

MT evaluation methods

WER

MT evaluation methods

PER

MT evaluation methods

technique

models

paraphrase classification accuracy

technique

paraphrase classification accuracy

models


Input: Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via Adaptive Simulated Annealing -LRB- ASA -RRB-, a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local deterministic search. We apply cluster analysis on the sampled parameter space to redetect the object and renew the local tracker. Our numerical hybrid local and global mode-seeking tracker is validated on challenging airborne videos with heavy occlusion and large camera motions. Our approach outperforms state-of-the-art trackers on the VIVID benchmark datasets.

object model

local optimization

local tracking

prediction techniques

Kalman filter

Adaptive Simulated Annealing -LRB- ASA -RRB-

exhaustive search

Monte Carlo approach

ASA

local deterministic search

cluster analysis

local tracker

numerical hybrid local and global mode-seeking tracker

black-box measure of similarity

model

it

method

approach

state-of-the-art trackers

visual object tracking

numerical optimization problem

object detection

global optimization problem

local mode of the similarity measure

parameter space of translation, rotation and scale

parameter space

sampled parameter space

heavy occlusion

camera motions

airborne videos

VIVID benchmark datasets

numerical optimization problem

visual object tracking

local optimization

local mode of the similarity measure

parameter space of translation, rotation and scale

local mode of the similarity measure

global optimization problem

object detection

Adaptive Simulated Annealing -LRB- ASA -RRB-

it

cluster analysis

sampled parameter space

cluster analysis

local tracker

Kalman filter

prediction techniques

method

exhaustive search

ASA

local deterministic search

state-of-the-art trackers

approach

ASA

Monte Carlo approach

airborne videos

numerical hybrid local and global mode-seeking tracker

VIVID benchmark datasets

approach

VIVID benchmark datasets

state-of-the-art trackers

heavy occlusion

airborne videos

camera motions

airborne videos

heavy occlusion

camera motions