Input: Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches. In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments. In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.

Techniques

baselines

automatically training modules

natural language generator

trainable components

hand-crafted template-based or rule-based approaches

trainable sentence planner

spoken dialogue system

hand-crafted template-based generation component

rule-based sentence planners

baseline sentence planners

trainable sentence planner

rule-based systems

hand-crafted system

quality of utterances

utterances

subjective human judgments

Techniques

automatically training modules

trainable sentence planner

spoken dialogue system

automatically training modules

natural language generator

utterances

trainable components

utterances

hand-crafted template-based or rule-based approaches

subjective human judgments

trainable sentence planner

trainable components

hand-crafted template-based or rule-based approaches

trainable sentence planner

rule-based systems

trainable sentence planner

baselines

trainable sentence planner

hand-crafted system

hand-crafted template-based generation component

rule-based sentence planners

rule-based sentence planners

baseline sentence planners

rule-based systems

baselines

baselines

hand-crafted system


Input: A new algorithm is proposed for novel view generation in one-to-one teleconferencing applications. Given the video streams acquired by two cameras placed on either side of a computer monitor, the proposed algorithm synthesises images from a virtual camera in arbitrary position -LRB- typically located within the monitor -RRB- to facilitate eye contact. Our technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel-view generation. The two main contributions of this paper are: i -RRB- a new type of three-plane graph for dense-stereo dynamic-programming, that encourages correct occlusion labeling; ii -RRB- a compact geometric derivation for novel-view synthesis by direct projection of the minimum-cost surface. Furthermore, this paper presents a novel algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts -LRB- flicker -RRB-; and a cost aggregation algorithm that acts directly on our three-dimensional matching cost space. Examples are given that demonstrate the robustness of the new algorithm to spatial and temporal artefacts for long stereo video streams. These include demonstrations of synthesis of cyclopean views of extended conversational sequences. We further demonstrate synthesis from a freely translating virtual camera.

algorithm

algorithm

technique

algorithm

algorithm

novel view generation

one-to-one teleconferencing applications

novel-view generation

occlusion labeling

novel-view synthesis

temporal maintenance of a background model

rendering of occlusions

synthesis of cyclopean views of extended conversational sequences

synthesis

video streams

images

long stereo video streams

extended conversational sequences

cameras

computer monitor

virtual camera

arbitrary position

eye contact

compact geometric derivation

temporal artefacts -LRB- flicker -RRB-

three-dimensional matching cost space

spatial and temporal artefacts

cyclopean views

translating virtual camera

dynamic-programming, stereo algorithm

three-plane graph

dense-stereo dynamic-programming

direct projection of the minimum-cost surface

cost aggregation algorithm

robustness

algorithm

novel view generation

novel view generation

one-to-one teleconferencing applications

cameras

video streams

algorithm

eye contact

virtual camera

images

technique

novel-view generation

dynamic-programming, stereo algorithm

technique

three-plane graph

dense-stereo dynamic-programming

dense-stereo dynamic-programming

occlusion labeling

compact geometric derivation

novel-view synthesis

direct projection of the minimum-cost surface

compact geometric derivation

algorithm

temporal maintenance of a background model

algorithm

rendering of occlusions

algorithm

temporal artefacts -LRB- flicker -RRB-

cost aggregation algorithm

three-dimensional matching cost space

algorithm

spatial and temporal artefacts

spatial and temporal artefacts

long stereo video streams

translating virtual camera

synthesis

arbitrary position

virtual camera

cost aggregation algorithm

algorithm

robustness

algorithm


Input: Manual acquisition of semantic constraints in broad domains is very expensive. This paper presents an automatic scheme for collecting statistics on cooccurrence patterns in a large corpus. To a large extent, these statistics reflect semantic constraints and thus are used to disambiguate anaphora references and syntactic ambiguities. The scheme was implemented by gathering statistics on the output of other linguistic tools. An experiment was performed to resolve references of the pronoun ``it'' in sentences that were randomly selected from the corpus. The results of the experiment show that in most of the cases the cooccurrence statistics indeed reflect the semantic constraints and thus provide a basis for a useful disambiguation tool.

Manual acquisition of semantic constraints

cooccurrence patterns

semantic constraints

anaphora references

syntactic ambiguities

cooccurrence statistics

semantic constraints

linguistic tools

disambiguation tool

semantic constraints

anaphora references

semantic constraints

syntactic ambiguities

cooccurrence statistics

disambiguation tool

anaphora references

syntactic ambiguities


Input: We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus -LRB- 100,000 words -RRB- and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.

method

approach

it

method

discovering parallel sentences

comparable, non-parallel corpora

parallel data

Chinese, Arabic, and English non-parallel newspaper corpora

parallel corpus

non-parallel corpus

scarce resources

maximum entropy classifier

statistical machine translation system

MT system

method

discovering parallel sentences

comparable, non-parallel corpora

discovering parallel sentences

approach

parallel data

it

statistical machine translation system

parallel corpus

MT system

non-parallel corpus

MT system

scarce resources

method

parallel data

Chinese, Arabic, and English non-parallel newspaper corpora

parallel corpus

non-parallel corpus


Input: In this paper, we describe a search procedure for statistical machine translation -LRB- MT -RRB- based on dynamic programming -LRB- DP -RRB-. Starting from a DP-based solution to the traveling salesman problem, we present a novel technique to restrict the possible word reordering between source and target language in order to achieve an efficient search algorithm. A search restriction especially useful for the translation direction from German to English is presented. The experimental tests are carried out on the Verbmobil task -LRB- German-English, 8000-word vocabulary -RRB-, which is a limited-domain spoken-language task.

search procedure

technique

statistical machine translation -LRB- MT -RRB-

limited-domain spoken-language task

dynamic programming -LRB- DP -RRB-

DP-based solution

traveling salesman problem

search algorithm

word reordering

Verbmobil task

German-English

search procedure

statistical machine translation -LRB- MT -RRB-

dynamic programming -LRB- DP -RRB-

statistical machine translation -LRB- MT -RRB-

technique

search algorithm

Verbmobil task

limited-domain spoken-language task


Input: A purely functional implementation of LR-parsers is given, together with a simple correctness proof. It is presented as a generalization of the recursive descent parser. For non-LR grammars the time-complexity of our parser is cubic if the functions that constitute the parser are implemented as memo-functions, i.e. functions that memorize the results of previous invocations. Memo-functions also facilitate a simple way to construct a very compact representation of the parse forest. For LR -LRB- 0 -RRB- grammars, our algorithm is closely related to the recursive ascent parsers recently discovered by Kruse-man Aretz -LSB- 1 -RSB- and Roberts -LSB- 2 -RSB-. Extended CF grammars -LRB- grammars with regular expressions at the right hand side -RRB- can be parsed with a simple modification of the LR-parser for normal CF grammars.

LR-parsers

correctness proof

recursive descent parser

non-LR grammars

parser

parser

memo-functions

Memo-functions

LR -LRB- 0 -RRB- grammars

Extended CF grammars

grammars

LR-parser

CF grammars

It

algorithm

time-complexity

parse forest

recursive ascent parsers

regular expressions

correctness proof

LR-parsers

algorithm

recursive ascent parsers

recursive descent parser

It

parser

non-LR grammars

memo-functions

parser

Memo-functions

parse forest

algorithm

LR -LRB- 0 -RRB- grammars

LR-parser

Extended CF grammars

LR-parser

CF grammars

time-complexity

parser

regular expressions

grammars


Input: In this paper we explore a new theory of discourse structure that stresses the role of purpose and processing in discourse. In this theory, discourse structure is composed of three separate but interrelated components: the structure of the sequence of utterances -LRB- called the linguistic structure -RRB-, a structure of purposes -LRB- called the intentional structure -RRB-, and the state of focus of attention -LRB- called the attentional state -RRB-. The linguistic structure consists of segments of the discourse into which the utterances naturally aggregate. The intentional structure captures the discourse-relevant purposes, expressed in each of the linguistic segments as well as relationships among them. The attentional state is an abstraction of the focus of attention of the participants as the discourse unfolds. The attentional state, being dynamic, records the objects, properties, and relations that are salient at each point of the discourse. The distinction among these components is essential to provide an adequate explanation of such discourse phenomena as cue phrases, referring expressions, and interruptions. The theory of attention, intention, and aggregation of utterances is illustrated in the paper with a number of example discourses. Various properties of discourse are described, and explanations for the behaviour of cue phrases, referring expressions, and interruptions are explored. This theory provides a framework for describing the processing of utterances in a discourse. Discourse processing requires recognizing how the utterances of the discourse aggregate into segments, recognizing the intentions expressed in the discourse and the relationships among intentions, and tracking the discourse through the operation of the mechanisms associated with attentional state. This processing description specifies in these recognition tasks the role of information from the discourse and from the participants' knowledge of the domain.

theory of discourse structure

theory of attention, intention, and aggregation of utterances

Discourse processing

discourse

discourse structure

linguistic structure

intentional structure

attentional state

linguistic structure

discourse

intentional structure

discourse-relevant purposes

attentional state

discourse

attentional state

discourse

discourse phenomena

cue phrases

referring expressions

interruptions

discourses

discourse

cue phrases

referring expressions

interruptions

discourse

discourse

discourse

discourse

attentional state

discourse

theory

components

theory

processing

recognition tasks

components

discourse structure

linguistic structure

components

intentional structure

components

attentional state

components

linguistic structure

intentional structure

intentional structure

attentional state

cue phrases

referring expressions

referring expressions

interruptions

intentional structure

discourse-relevant purposes

cue phrases

discourse phenomena

referring expressions

discourse phenomena

interruptions

discourse phenomena


Input: We examine the relationship between the two grammatical formalisms: Tree Adjoining Grammars and Head Grammars. We briefly investigate the weak equivalence of the two formalisms. We then turn to a discussion comparing the linguistic expressiveness of the two formalisms.

grammatical formalisms

Tree Adjoining Grammars

Head Grammars

formalisms

formalisms

linguistic expressiveness

Tree Adjoining Grammars

grammatical formalisms

Head Grammars

grammatical formalisms

Tree Adjoining Grammars

Head Grammars

linguistic expressiveness

formalisms


Input: We provide a unified account of sentence-level and text-level anaphora within the framework of a dependency-based grammar model. Criteria for anaphora resolution within sentence boundaries rephrase major concepts from GB's binding theory, while those for text-level anaphora incorporate an adapted version of a Grosz-Sidner-style focus model.

sentence-level and text-level anaphora

text-level anaphora

dependency-based grammar model

GB's binding theory

Grosz-Sidner-style focus model

Criteria

those

anaphora resolution within sentence boundaries

dependency-based grammar model

sentence-level and text-level anaphora

Criteria

anaphora resolution within sentence boundaries

GB's binding theory

Criteria

those

text-level anaphora

Grosz-Sidner-style focus model

those


Input: Coedition of a natural language text and its representation in some interlingual form seems the best and simplest way to share text revision across languages. For various reasons, UNL graphs are the best candidates in this context. We are developing a prototype where, in the simplest sharing scenario, naive users interact directly with the text in their language -LRB- L0 -RRB-, and indirectly with the associated graph. The modified graph is then sent to the UNL-L0 deconverter and the result shown. If is is satisfactory, the errors were probably due to the graph, not to the deconverter, and the graph is sent to deconverters in other languages. Versions in some other languages known by the user may be displayed, so that improvement sharing is visible and encouraging. As new versions are added with appropriate tags and attributes in the original multilingual document, nothing is ever lost, and cooperative working on a document is rendered feasible. On the internal side, liaisons are established between elements of the text and the graph by using broadly available resources such as a LO-English or better a L0-UNL dictionary, a morphosyntactic parser of L0, and a canonical graph2tree transformation. Establishing a ``best'' correspondence between the'' UNL-tree + L0'' and the'' MS-L0 structure'', a lattice, may be done using the dictionary and trying to align the tree and the selected trajectory with as few crossing liaisons as possible. A central goal of this research is to merge approaches from pivot MT, interactive MT, and multilingual text authoring.

Coedition

pivot MT

interactive MT

multilingual text authoring

natural language text

languages

original multilingual document

LO-English or better a L0-UNL dictionary

dictionary

text revision

UNL graphs

graph

graph

graph

graph

liaisons

canonical graph2tree transformation

UNL-tree + L0

MS-L0 structure

lattice

crossing liaisons

UNL-L0 deconverter

deconverter

deconverters

morphosyntactic parser of L0

resources

Coedition

text revision

natural language text

Coedition

graph

UNL-L0 deconverter

resources

liaisons

dictionary

lattice

LO-English or better a L0-UNL dictionary

resources

morphosyntactic parser of L0

resources

canonical graph2tree transformation

resources

LO-English or better a L0-UNL dictionary

morphosyntactic parser of L0

morphosyntactic parser of L0

canonical graph2tree transformation

UNL-tree + L0

MS-L0 structure

pivot MT

interactive MT

interactive MT

multilingual text authoring


Input: The reality of analogies between words is refuted by noone -LRB- e.g., I walked is to to walk as I laughed is to to laugh, noted I walked: to walk:: I laughed: to laugh -RRB-. But computational linguists seem to be quite dubious about analogies between sentences: they would not be enough numerous to be of any use. We report experiments conducted on a multilingual corpus to estimate the number of analogies among the sentences that it contains. We give two estimates, a lower one and a higher one. As an analogy must be valid on the level of form as well as on the level of meaning, we relied on the idea that translation should preserve meaning to test for similar meanings.

analogies between words

analogies between sentences

multilingual corpus

analogies

analogy

multilingual corpus

analogies


Input: Our goal is to learn a Mahalanobis distance by minimizing a loss defined on the weighted sum of the precision at different ranks. Our core motivation is that minimizing a weighted rank loss is a natural criterion for many problems in computer vision such as person re-identification. We propose a novel metric learning formulation called Weighted Approximate Rank Component Analysis -LRB- WARCA -RRB-. We then derive a scalable stochastic gradient descent algorithm for the resulting learning problem. We also derive an efficient non-linear extension of WARCA by using the kernel trick. Kernel space embedding decouples the training and prediction costs from the data dimension and enables us to plug inarbitrary distance measures which are more natural for the features. We also address a more general problem of matrix rank degeneration & non-isolated minima in the low-rank matrix optimization by using new type of regularizer which approximately enforces the or-thonormality of the learned matrix very efficiently. We validate this new method on nine standard person re-identification datasets including two large scale Market-1501 and CUHK03 datasets and show that we improve upon the current state-of-the-art methods on all of them.

Mahalanobis distance

computer vision

person re-identification

low-rank matrix optimization

loss

weighted sum

ranks

weighted rank loss

Kernel space embedding

training and prediction costs

data dimension

features

matrix rank degeneration

non-isolated minima

or-thonormality

learned matrix

precision

metric learning formulation

Weighted Approximate Rank Component Analysis -LRB- WARCA -RRB-

stochastic gradient descent algorithm

non-linear extension of WARCA

WARCA

kernel trick

inarbitrary distance measures

regularizer

learning problem

method

them

person re-identification datasets

scale Market-1501

CUHK03 datasets

loss

Mahalanobis distance

weighted rank loss

computer vision

weighted rank loss

person re-identification

stochastic gradient descent algorithm

learning problem

kernel trick

non-linear extension of WARCA

Kernel space embedding

inarbitrary distance measures

regularizer

low-rank matrix optimization

regularizer

or-thonormality

weighted sum

precision

matrix rank degeneration

low-rank matrix optimization

non-isolated minima

low-rank matrix optimization

or-thonormality

learned matrix

person re-identification

computer vision

Weighted Approximate Rank Component Analysis -LRB- WARCA -RRB-

metric learning formulation

scale Market-1501

person re-identification datasets

CUHK03 datasets

person re-identification datasets

matrix rank degeneration

non-isolated minima

CUHK03 datasets

scale Market-1501

person re-identification datasets

method


Input: In this paper, we discuss language model adaptation methods given a word list and a raw corpus. In this situation, the general method is to segment the raw corpus automatically using a word list, correct the output sentences by hand, and build a model from the segmented corpus. In this sentence-by-sentence error correction method, however, the annotator encounters grammatically complicated positions and this results in a decrease of productivity. In this paper, we propose to concentrate on correcting the positions in which the words in the list appear by taking a word as a correction unit. This method allows us to avoid these problems and go directly to capturing the statistical behavior of specific words in the application. In the experiments, we used a variety of methods for preparing a segmented corpus and compared the language models by their speech recognition accuracies. The results showed the advantages of our method.

language model adaptation methods

sentence-by-sentence error correction method

language models

word list

word list

raw corpus

raw corpus

segmented corpus

method

model

method

methods

method

preparing a segmented corpus

speech recognition accuracies

word list

language model adaptation methods

raw corpus

language model adaptation methods

method

raw corpus

word list

method

segmented corpus

model

methods

preparing a segmented corpus

word list

raw corpus

speech recognition accuracies

language models


Input: Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions. In all of these cases, we expect some form of dependency between the draws: the nucleotide at one position in the DNA strand may depend on the preceding nucleotides, children's names are highly correlated from year to year, and topics in text may be correlated and dynamic. These dependencies are not naturally captured by the typical Dirichlet-multinomial formulation. Here, we leverage a logistic stick-breaking representation and recent innovations in Pólya-gamma augmentation to reformu-late the multinomial distribution in terms of latent variables with jointly Gaussian likelihoods, enabling us to take advantage of a host of Bayesian inference techniques for Gaussian models with minimal overhead.

modeling problems

Pólya-gamma augmentation

discrete data

nucleotides in a DNA sequence

text documents

multinomial or categorical distributions

multinomial distributions

Dirichlet-multinomial formulation

logistic stick-breaking representation

multinomial distribution

Bayesian inference techniques

Gaussian models

nucleotide

DNA strand

preceding nucleotides

latent variables

jointly Gaussian likelihoods

minimal overhead

discrete data

modeling problems

multinomial or categorical distributions

modeling problems

multinomial distributions

nucleotides in a DNA sequence

multinomial distributions

text documents

logistic stick-breaking representation

multinomial distribution

Pólya-gamma augmentation

multinomial distribution

Bayesian inference techniques

Gaussian models

latent variables

multinomial distribution

jointly Gaussian likelihoods

latent variables

minimal overhead

Gaussian models


Input: MINPRAN, a new robust operator, nds good ts in data sets where more than 50% of the points are outliers. Unlike other techniques that handle large outlier percentages, MINPRAN does not rely on a known error bound for the good data. Instead it assumes that the bad data are randomly -LRB- uniformly -RRB- distributed within the dynamic range of the sensor. Based on this, MINPRAN uses random sampling to search for the t and the number of inliers to the t that are least likely to have occurred randomly. It runs in time O -LRB- N 2 + SN log N -RRB-, where S is the number of random samples and N is the number of data points. We demonstrate analytically that MINPRAN distinguishes good ts from ts to random data, and that MINPRAN nds accurate ts and nearly the correct number of inliers, regardless of the percentage of true inliers. MINPRAN's properties are connrmed experimentally on synthetic data and compare favorably to least median of squares. Related work applies MINPRAN to complex range and intensity data 23 -RSB-.

MINPRAN

robust operator

MINPRAN

MINPRAN

random sampling

MINPRAN

MINPRAN

MINPRAN

least median of squares

MINPRAN

techniques

it

It

large outlier percentages

percentage of true inliers

error bound

dynamic range of the sensor

complex range

synthetic data

intensity data

MINPRAN

robust operator

techniques

large outlier percentages

random sampling

MINPRAN

MINPRAN

complex range

MINPRAN

intensity data

techniques

MINPRAN

least median of squares

MINPRAN

synthetic data

MINPRAN


Input: Metagrammatical formalisms that combine context-free phrase structure rules and metarules -LRB- MPS grammars -RRB- allow concise statement of generalizations about the syntax of natural languages. Unconstrained MPS grammars, unfortunately, are not computationally safe. We evaluate several proposals for constraining them, basing our assessment on computational tractability and explanatory adequacy. We show that none of them satisfies both criteria, and suggest new directions for research on alternative metagrammatical formalisms.

Metagrammatical formalisms

Unconstrained MPS grammars

metagrammatical formalisms

context-free phrase structure rules

metarules -LRB- MPS grammars -RRB-

syntax of natural languages

them

them

criteria

computational tractability and explanatory adequacy

context-free phrase structure rules

Metagrammatical formalisms

metarules -LRB- MPS grammars -RRB-

Metagrammatical formalisms

context-free phrase structure rules

metarules -LRB- MPS grammars -RRB-

computational tractability and explanatory adequacy

them


Input: The unique properties of tree-adjoining grammars -LRB- TAG -RRB- present a challenge for the application of TAGs beyond the limited confines of syntax, for instance, to the task of semantic interpretation or automatic translation of natural language. We present a variant of TAGs, called synchronous TAGs, which characterize correspondences between languages. The formalism's intended usage is to relate expressions of natural languages to their associated semantics represented in a logical form language, or to their translates in another natural language; in summary, we intend it to allow TAGs to be used beyond their role in syntax proper. We discuss the application of synchronous TAGs to concrete examples, mentioning primarily in passing some computational issues that arise in its interpretation.

tree-adjoining grammars -LRB- TAG -RRB-

TAGs

variant of TAGs

TAGs

synchronous TAGs

TAGs

synchronous TAGs

syntax

expressions of natural languages

semantics

logical form language

natural language

syntax proper

semantic interpretation

automatic translation of natural language

TAGs

semantic interpretation

TAGs

automatic translation of natural language

logical form language

semantics

TAGs

syntax proper

semantic interpretation

automatic translation of natural language


Input: A model-based approach to on-line cursive handwriting analysis and recognition is presented and evaluated. In this model, on-line handwriting is considered as a modulation of a simple cycloidal pen motion, described by two coupled oscillations with a constant linear drift along the line of the writing. By slow modulations of the amplitudes and phase lags of the two oscillators, a general pen trajectory can be efficiently encoded. These parameters are then quantized into a small number of values without altering the writing intelligibility. A general procedure for the estimation and quantization of these cycloidal motion parameters for arbitrary handwriting is presented. The result is a discrete motor control representation of the continuous pen motion, via the quantized levels of the model parameters. This motor control representation enables successful word spotting and matching of cursive scripts. Our experiments clearly indicate the potential of this dynamic representation for complete cursive handwriting recognition.

model-based approach

discrete motor control representation

motor control representation

dynamic representation

on-line cursive handwriting analysis and recognition

on-line handwriting

cycloidal pen motion

word spotting

matching of cursive scripts

cursive handwriting recognition

model

constant linear drift

pen trajectory

writing intelligibility

cycloidal motion parameters

continuous pen motion

arbitrary handwriting

model-based approach

on-line cursive handwriting analysis and recognition

model

on-line handwriting

cycloidal motion parameters

arbitrary handwriting

discrete motor control representation

continuous pen motion

motor control representation

word spotting

motor control representation

matching of cursive scripts

dynamic representation

cursive handwriting recognition

on-line handwriting

cycloidal pen motion

word spotting

matching of cursive scripts


Input: In the Object Recognition task, there exists a di-chotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep archi-tectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose estimation using these approaches has received relatively less attention. In this work, we study how Convolutional Neural Networks -LRB- CNN -RRB- architectures can be adapted to the task of simultaneous object recognition and pose estimation. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations within CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets and we achieve better than the state-of-the-art.

Object Recognition task

categorization of objects

estimating object pose

object category recognition

object pose estimation

object recognition

pose estimation

former

latter

representation

task

approaches

them

this

view-invariant representation

deep archi-tectures

Deep learning methods

Convolutional Neural Networks -LRB- CNN -RRB- architectures

CNN models

layers of distributed representations

CNNs

object category representations

pose information

layers

object pose information

multi-view datasets

categorization of objects

Object Recognition task

estimating object pose

Object Recognition task

layers

CNN models

layers of distributed representations

CNNs

categorization of objects

estimating object pose

object recognition

pose estimation

view-invariant representation

former

representation

latter

representation

pose information

deep archi-tectures

object category recognition

approaches

object pose estimation

Convolutional Neural Networks -LRB- CNN -RRB- architectures

object recognition

Convolutional Neural Networks -LRB- CNN -RRB- architectures

pose estimation

layers of distributed representations

object pose information

this

object category representations


Input: In this paper we present our recent work on harvesting English-Chinese bitexts of the laws of Hong Kong from the Web and aligning them to the subparagraph level via utilizing the numbering system in the legal text hierarchy. Basic methodology and practical techniques are reported in detail. The resultant bilingual corpus, 10.4 M English words and 18.3 M Chinese characters, is an authoritative and comprehensive text collection covering the specific and special domain of HK laws. It is particularly valuable to empirical MT research. This piece of work has also laid a foundation for exploring and harvesting English-Chinese bitexts in a larger volume from the Web.

English-Chinese bitexts

bilingual corpus

English-Chinese bitexts

them

It

numbering system

legal text hierarchy

empirical MT research

It

empirical MT research


Input: Light fields are image-based representations that use densely sampled rays as a scene description. In this paper, we explore geometric structures of 3D lines in ray space for improving light field triangulation and stereo matching. The triangulation problem aims to fill in the ray space with continuous and non-overlapping simplices anchored at sampled points -LRB- rays -RRB-. Such a triangulation provides a piecewise-linear interpolant useful for light field super-resolution. We show that the light field space is largely bi-linear due to 3D line segments in the scene, and direct tri-angulation of these bilinear subspaces leads to large errors. We instead present a simple but effective algorithm to first map bilinear subspaces to line constraints and then apply Constrained Delaunay Triangulation -LRB- CDT -RRB-. Based on our analysis, we further develop a novel line-assisted graph-cut -LRB- LAGC -RRB- algorithm that effectively encodes 3D line constraints into light field stereo matching. Experiments on synthetic and real data show that both our triangulation and LAGC algorithms outperform state-of-the-art solutions in accuracy and visual quality.

Light fields

densely sampled rays

scene description

ray space

ray space

continuous and non-overlapping simplices

triangulation

piecewise-linear interpolant

light field space

3D line segments

bilinear subspaces

bilinear subspaces

line constraints

3D line constraints

image-based representations

Constrained Delaunay Triangulation -LRB- CDT -RRB-

line-assisted graph-cut -LRB- LAGC -RRB- algorithm

triangulation and LAGC algorithms

geometric structures of 3D lines

light field triangulation

stereo matching

triangulation problem

light field super-resolution

light field stereo matching

synthetic and real data

state-of-the-art solutions

accuracy

visual quality

geometric structures of 3D lines

light field triangulation

geometric structures of 3D lines

stereo matching

triangulation

piecewise-linear interpolant

piecewise-linear interpolant

light field super-resolution

ray space

geometric structures of 3D lines

light field triangulation

stereo matching

synthetic and real data

triangulation and LAGC algorithms

synthetic and real data

state-of-the-art solutions

accuracy

triangulation and LAGC algorithms

accuracy

state-of-the-art solutions

visual quality

triangulation and LAGC algorithms

visual quality

state-of-the-art solutions

triangulation and LAGC algorithms

state-of-the-art solutions


Input: This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. A method for producing such phrases from a word-aligned corpora is proposed. A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. Translations are produced by means of a beam-search decoder. Experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data.

phrase-based statistical machine translation method

statistical translation model

training method

beam-search decoder

non-contiguous phrases

word-aligned corpora

method

phrases

phrases

method

maximization of translation accuracy

NIST evaluation metric

Translations

non-contiguous phrases

phrase-based statistical machine translation method

method

phrases

statistical translation model

phrases

maximization of translation accuracy

training method

beam-search decoder

Translations

word-aligned corpora

method

NIST evaluation metric

statistical translation model


Input: GLOSSER is designed to support reading and learning to read in a foreign language. There are four language pairs currently supported by GLOSSER: English-Bulgarian, English-Estonian, English-Hungarian and French-Dutch. The program is operational on UNIX and Windows'95 platforms, and has undergone a pilot user-study. A demonstration -LRB- in UNIX -RRB- for Applied Natural Language Processing emphasizes components put to novel technical uses in intelligent computer-assisted morphological analysis -LRB- ICALL -RRB-, including disambiguated morphological analysis and lemmatized indexing for an aligned bilingual corpus of word examples.

GLOSSER

GLOSSER

disambiguated morphological analysis

lemmatized indexing

reading and learning

Applied Natural Language Processing

intelligent computer-assisted morphological analysis -LRB- ICALL -RRB-

language pairs

program

components

English-Bulgarian

English-Estonian

English-Hungarian

French-Dutch

aligned bilingual corpus

UNIX and Windows'95 platforms

user-study

GLOSSER

reading and learning

language pairs

GLOSSER

components

intelligent computer-assisted morphological analysis -LRB- ICALL -RRB-

disambiguated morphological analysis

aligned bilingual corpus

lemmatized indexing

aligned bilingual corpus

English-Bulgarian

language pairs

English-Estonian

language pairs

English-Hungarian

language pairs

French-Dutch

language pairs

disambiguated morphological analysis

components

lemmatized indexing

components

English-Bulgarian

English-Estonian

English-Estonian

English-Hungarian

English-Hungarian

French-Dutch

disambiguated morphological analysis

lemmatized indexing


Input: We present a new part-of-speech tagger that demonstrates the following ideas: -LRB- i -RRB- explicit use of both preceding and following tag contexts via a dependency network representation, -LRB- ii -RRB- broad use of lexical features, including jointly conditioning on multiple consecutive words, -LRB- iii -RRB- effective use of priors in conditional loglinear models, and -LRB- iv -RRB- fine-grained modeling of unknown word features. Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result.

part-of-speech tagger

dependency network representation

fine-grained modeling of unknown word features

tagger

tag contexts

lexical features

multiple consecutive words

priors in conditional loglinear models

accuracy

error

Penn Treebank WSJ

tagging

tag contexts

part-of-speech tagger

dependency network representation

tag contexts

lexical features

part-of-speech tagger

priors in conditional loglinear models

part-of-speech tagger

fine-grained modeling of unknown word features

part-of-speech tagger

accuracy

tagger

Penn Treebank WSJ

tagger

error

tagger


Input: Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks -LRB- CNN -RRB-'s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive -LRB- i.e. intra-class -RRB- training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.

Person re-identification

person re-identification

person re-identification

person re-identification

person re-identification

pose

illumination

occlusion

camera view

feature space

feature extraction

geodesic distance

Euclidean distance

Euclidean distance

local range

graphical relationship

geodesic distance

local range

CNN embedding

intra-class variations

metric weight constraint

learned metric

generalization ability

robust deep metrics

pedestrian data

highly-curved manifolds

convolutional neural networks -LRB- CNN -RRB-

deep embedding methods

manifold learning methods

moderate positive sample mining method

robust CNN

deep model

deep models

data

learning

state-of-the-art methods

highly-curved manifolds

pedestrian data

convolutional neural networks -LRB- CNN -RRB-

feature extraction

Euclidean distance

deep embedding methods

Euclidean distance

manifold learning methods

Euclidean distance

geodesic distance

graphical relationship

geodesic distance

moderate positive sample mining method

robust CNN

robust CNN

person re-identification

metric weight constraint

learning

robust deep metrics

person re-identification

deep model

person re-identification

state-of-the-art methods

person re-identification

deep models

person re-identification

feature space

highly-curved manifolds

local range

Euclidean distance

intra-class variations

data

generalization ability

learned metric

Euclidean distance

graphical relationship

deep model

state-of-the-art methods


Input: Utterance Verification -LRB- UV -RRB- is a critical function of an Automatic Speech Recognition -LRB- ASR -RRB- System working on real applications where spontaneous speech, out-of-vocabulary -LRB- OOV -RRB- words and acoustic noises are present. In this paper we present a new UV procedure with two major features: a -RRB- Confidence tests are applied to decoded string hypotheses obtained from using word and garbage models that represent OOV words and noises. Thus the ASR system is designed to deal with what we refer to as Word Spotting and Noise Spotting capabilities. b -RRB- The UV procedure is based on three different confidence tests, two based on acoustic measures and one founded on linguistic information, applied in a hierarchical structure. Experimental results from a real telephone application on a natural number recognition task show an 50% reduction in recognition errors with a moderate 12% rejection rate of correct utterances and a low 1.5% rate of false acceptance.

Utterance Verification -LRB- UV -RRB-

Automatic Speech Recognition -LRB- ASR -RRB- System

UV procedure

Confidence tests

ASR system

UV procedure

confidence tests

spontaneous speech

out-of-vocabulary -LRB- OOV -RRB- words

acoustic noises

decoded string hypotheses

OOV words

noises

linguistic information

hierarchical structure

Word Spotting

Noise Spotting capabilities

telephone application

natural number recognition task

two

one

acoustic measures

recognition errors

rejection rate

false acceptance

Utterance Verification -LRB- UV -RRB-

Automatic Speech Recognition -LRB- ASR -RRB- System

two

confidence tests

one

confidence tests

Confidence tests

decoded string hypotheses

ASR system

Word Spotting

ASR system

Noise Spotting capabilities

confidence tests

UV procedure

confidence tests

hierarchical structure

acoustic measures

two

linguistic information

one

noises

OOV words

natural number recognition task

telephone application

recognition errors

natural number recognition task


Input: A critical step in encoding sound for neuronal processing occurs when the analog pressure wave is coded into discrete nerve-action potentials. Recent pool models of the inner hair cell synapse do not reproduce the dead time period after an intense stimulus, so we used visual inspection and automatic speech recognition -LRB- ASR -RRB- to investigate an offset adaptation -LRB- OA -RRB- model proposed by Zhang et al. -LSB- 1 -RSB-. OA improved phase locking in the auditory nerve -LRB- AN -RRB- and raised ASR accuracy for features derived from AN fibers -LRB- ANFs -RRB-. We also found that OA is crucial for auditory processing by onset neurons -LRB- ONs -RRB- in the next neuronal stage, the auditory brainstem. Multi-layer perceptrons -LRB- MLPs -RRB- performed much better than standard Gaussian mixture models -LRB- GMMs -RRB- for both our ANF-based and ON-based auditory features. Similar results were previously obtained with MSG -LRB- Modulation-filtered Spec-troGram -RRB- auditory features -LSB- 2 -RSB-. Thus we believe researchers working with novel features should consider trying MLPs.

encoding sound

neuronal processing

phase locking in the auditory nerve -LRB- AN -RRB-

auditory processing

analog pressure wave

discrete nerve-action potentials

inner hair cell synapse

features

AN fibers -LRB- ANFs -RRB-

onset neurons -LRB- ONs -RRB-

auditory brainstem

ANF-based and ON-based auditory features

MSG -LRB- Modulation-filtered Spec-troGram -RRB- auditory features

features

pool models

visual inspection

automatic speech recognition -LRB- ASR -RRB-

offset adaptation -LRB- OA -RRB- model

OA

OA

Multi-layer perceptrons -LRB- MLPs -RRB-

Gaussian mixture models -LRB- GMMs -RRB-

MLPs

ASR accuracy

encoding sound

neuronal processing

discrete nerve-action potentials

analog pressure wave

pool models

inner hair cell synapse

visual inspection

offset adaptation -LRB- OA -RRB- model

automatic speech recognition -LRB- ASR -RRB-

offset adaptation -LRB- OA -RRB- model

OA

phase locking in the auditory nerve -LRB- AN -RRB-

OA

features

AN fibers -LRB- ANFs -RRB-

features

OA

auditory processing

onset neurons -LRB- ONs -RRB-

OA

Multi-layer perceptrons -LRB- MLPs -RRB-

ANF-based and ON-based auditory features

Gaussian mixture models -LRB- GMMs -RRB-

ANF-based and ON-based auditory features

visual inspection

automatic speech recognition -LRB- ASR -RRB-

ASR accuracy

features

Multi-layer perceptrons -LRB- MLPs -RRB-

Gaussian mixture models -LRB- GMMs -RRB-


Input: Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we present an approach to rapidly creating pixel-accurate semantic label maps for images extracted from modern computer games.

computer vision

high-capacity models

large datasets

large datasets

images

pixel-level labels

pixel-accurate semantic label maps

modern computer games

approach

high-capacity models

computer vision

large datasets

high-capacity models

approach

pixel-accurate semantic label maps

pixel-accurate semantic label maps

images

pixel-level labels

large datasets

images

modern computer games


Input: We propose a novel step toward the unsupervised seg-mentation of whole objects by combining'' hints'' of partial scene segmentation offered by multiple soft, binary mattes. These mattes are implied by a set of hypothesized object boundary fragments in the scene. Rather than trying to find or define a single'' best'' segmentation, we generate multiple segmentations of an image. This reflects contemporary methods for unsupervised object discovery from groups of images, and it allows us to define intuitive evaluation met-rics for our sets of segmentations based on the accurate and parsimonious delineation of scene objects. Our proposed approach builds on recent advances in spectral clustering, image matting, and boundary detection. It is demonstrated qualitatively and quantitatively on a dataset of scenes and is suitable for current work in unsupervised object discovery without top-down knowledge.

unsupervised seg-mentation of whole objects

partial scene segmentation

unsupervised object discovery

unsupervised object discovery

soft, binary mattes

hypothesized object boundary fragments

delineation of scene objects

mattes

contemporary methods

approach

It

spectral clustering

image matting

boundary detection

dataset of scenes

partial scene segmentation

unsupervised seg-mentation of whole objects

soft, binary mattes

partial scene segmentation

hypothesized object boundary fragments

mattes

contemporary methods

unsupervised object discovery

spectral clustering

approach

image matting

approach

boundary detection

approach

It

unsupervised object discovery

spectral clustering

image matting

image matting

boundary detection

dataset of scenes

It


Input: Language resource quality is crucial in NLP. Many of the resources used are derived from data created by human beings out of an NLP context, especially regarding MT and reference translations. Indeed, automatic evaluations need high-quality data that allow the comparison of both automatic and human translations. The validation of these resources is widely recommended before being used. This paper describes the impact of using different-quality references on evaluation. Surprisingly enough, similar scores are obtained in many cases regardless of the quality. Thus, the limitations of the automatic metrics used within MT are also discussed in this regard.

Language resource quality

automatic metrics

NLP

NLP

MT

reference translations

automatic evaluations

MT

high-quality data

resources

evaluation

different-quality references

Language resource quality

NLP

MT

NLP

reference translations

NLP

MT

reference translations

high-quality data

automatic evaluations

automatic metrics

MT

different-quality references

evaluation


Input: This poster paper describes a full scale two-level morphological description -LRB- Karttunen, 1983; Koskenniemi, 1983 -RRB- of Turkish word structures. The description has been implemented using the PC-KIMMO environment -LRB- Antworth, 1990 -RRB- and is based on a root word lexicon of about 23,000 roots words. Almost all the special cases of and exceptions to phonological and morphological rules have been implemented. Turkish is an agglutinative language with word structures formed by productive affixations of derivational and inflectional suffixes to root words. Turkish has finite-state but nevertheless rather complex morphotactics. Morphemes added to a root word or a stem can convert the word from a nominal to a verbal structure or vice-versa, or can create adverbial constructs. The surface realizations of morphological constructions are constrained and modified by a number of phonetic rules such as vowel harmony.

full scale two-level morphological description

surface realizations of morphological constructions

Turkish word structures

root word lexicon

Turkish

agglutinative language

Turkish

description

PC-KIMMO environment

phonological and morphological rules

word structures

productive affixations of derivational and inflectional suffixes

finite-state

Morphemes

verbal structure

adverbial constructs

phonetic rules

vowel harmony

full scale two-level morphological description

Turkish word structures

PC-KIMMO environment

description

root word lexicon

description

phonetic rules

surface realizations of morphological constructions

Turkish

agglutinative language

vowel harmony

phonetic rules

word structures

agglutinative language

productive affixations of derivational and inflectional suffixes

word structures


Input: This paper deals with the problem of generating the fundamental frequency -LRB- F0 -RRB- contour of speech from a text input for text-to-speech synthesis. We have previously introduced a statistical model describing the generating process of speech F0 contours, based on the discrete-time version of the Fujisaki model. One remarkable feature of this model is that it has allowed us to derive an efficient algorithm based on powerful statistical methods for estimating the Fujisaki-model parameters from raw F0 contours. To associate a sequence of the Fujisaki-model parameters with a text input based on statistical learning, this paper proposes extending this model to a context-dependent one. We further propose a parameter training algorithm for the present model based on a decision tree-based context clustering.

fundamental frequency -LRB- F0 -RRB- contour of speech

speech F0 contours

remarkable feature

Fujisaki-model parameters

raw F0 contours

Fujisaki-model parameters

text input

text input

text-to-speech synthesis

statistical model

Fujisaki model

statistical methods

statistical learning

parameter training algorithm

decision tree-based context clustering

model

it

algorithm

model

model

fundamental frequency -LRB- F0 -RRB- contour of speech

text-to-speech synthesis

text input

fundamental frequency -LRB- F0 -RRB- contour of speech

statistical model

speech F0 contours

Fujisaki model

statistical model

algorithm

Fujisaki-model parameters

statistical methods

algorithm

raw F0 contours

Fujisaki-model parameters

text input

Fujisaki-model parameters

statistical learning

Fujisaki-model parameters

parameter training algorithm

model

decision tree-based context clustering

parameter training algorithm

remarkable feature

model


Input: We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-and-conquer procedure in the space of candidate regions. Compared to the exhaustive procedure that thus far is the state-of-the-art for cascade evaluation, the proposed method requires fewer evaluations of the classifier functions, thereby speeding up the search. Furthermore, we show how the recently developed efficient subwindow search -LRB- ESS -RRB- procedure -LSB- 11 -RSB- can be integrated into the last stage of our method. This allows us to use our method to act not only as a faster procedure for cascade evaluation, but also as a tool to perform efficient branch-and-bound object detection with nonlinear quality functions, in particular kernel-ized support vector machines. Experiments on the PASCAL VOC 2006 dataset show an acceleration of more than 50% by our method compared to standard cascade evaluation.

method

method

method

method

method

evaluation of object detection cascades

cascade evaluation

search

cascade evaluation

branch-and-bound object detection

divide-and-conquer procedure

space of candidate regions

exhaustive procedure

subwindow search -LRB- ESS -RRB- procedure

kernel-ized support vector machines

cascade evaluation

classifier functions

nonlinear quality functions

PASCAL VOC 2006 dataset

method

evaluation of object detection cascades

divide-and-conquer procedure

method

exhaustive procedure

cascade evaluation

method

search

method

cascade evaluation

method

branch-and-bound object detection

nonlinear quality functions

branch-and-bound object detection

space of candidate regions

divide-and-conquer procedure

exhaustive procedure

method

cascade evaluation

method

subwindow search -LRB- ESS -RRB- procedure

method

kernel-ized support vector machines

nonlinear quality functions

PASCAL VOC 2006 dataset

method

PASCAL VOC 2006 dataset

cascade evaluation


Input: Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.

Background modeling

vision systems

detection

modeling and subtraction of such scenes

modeling of the dynamic characteristics

computation of features

density estimation

static or quasi-static structures

persistent dynamic behavior

optical flow

feature

higher dimensional space

ambiguities

data-dependent bandwidth

scene

method

scenes

approach

kernels

Background modeling

vision systems

persistent dynamic behavior

scene

higher dimensional space

feature

ambiguities

computation of features

method

modeling and subtraction of such scenes

optical flow

modeling of the dynamic characteristics

optical flow

feature

feature

modeling of the dynamic characteristics

data-dependent bandwidth

ambiguities

data-dependent bandwidth

density estimation

kernels

density estimation


Input: Information distillation aims to extract relevant pieces of information related to a given query from massive, possibly multilingual, audio and textual document sources. In this paper, we present our approach for using information extraction annotations to augment document retrieval for distillation. We take advantage of the fact that some of the distillation queries can be associated with annotation elements introduced for the NIST Automatic Content Extraction -LRB- ACE -RRB- task. We experimentally show that using the ACE events to constrain the document set returned by an information retrieval engine significantly improves the precision at various recall rates for two different query templates.

Information distillation

document retrieval for distillation

NIST Automatic Content Extraction -LRB- ACE -RRB- task

massive, possibly multilingual, audio and textual document sources

information extraction annotations

distillation queries

annotation elements

ACE events

information retrieval engine

precision

recall rates

information extraction annotations

document retrieval for distillation


Input: This paper presents a novel representation for three-dimensional objects in terms of affine-invariant image patches and their spatial relationships. Multi-view constraints associated with groups of patches are combined with a normalized representation of their appearance to guide matching and reconstruction, allowing the acquisition of true three-dimensional affine and Euclidean models from multiple images and their recognition in a single photograph taken from an arbitrary viewpoint. The proposed approach does not require a separate segmentation stage and is applicable to cluttered scenes. Preliminary modeling and recognition results are presented.

representation

approach

three-dimensional objects

affine-invariant image patches

spatial relationships

Multi-view constraints

cluttered scenes

normalized representation

segmentation stage

matching

reconstruction

acquisition of true three-dimensional affine and Euclidean models

recognition

images

representation

three-dimensional objects

Multi-view constraints

matching

Multi-view constraints

reconstruction

normalized representation

matching

normalized representation

reconstruction

images

acquisition of true three-dimensional affine and Euclidean models

approach

cluttered scenes

affine-invariant image patches

three-dimensional objects

spatial relationships

affine-invariant image patches

Multi-view constraints

normalized representation

matching

reconstruction


Input: Fast algorithms for nearest neighbor -LRB- NN -RRB- search have in large part focused on 2 distance. Here we develop an approach for 1 distance that begins with an explicit and exactly distance-preserving embedding of the points into 2 2. We show how this can efficiently be combined with random-projection based methods for 2 NN search, such as locality-sensitive hashing -LRB- LSH -RRB- or random projection trees. We rigorously establish the correctness of the methodology and show by experimentation using LSH that it is competitive in practice with available alternatives.

Fast algorithms

approach

this

it

alternatives

nearest neighbor -LRB- NN -RRB- search

NN search

distance

1 distance

distance-preserving embedding

random-projection based methods

locality-sensitive hashing -LRB- LSH -RRB-

random projection trees

LSH

Fast algorithms

nearest neighbor -LRB- NN -RRB- search

approach

1 distance

random-projection based methods

NN search

this

random-projection based methods

locality-sensitive hashing -LRB- LSH -RRB-

random projection trees

locality-sensitive hashing -LRB- LSH -RRB-

random-projection based methods

random projection trees

random-projection based methods

it

alternatives