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11267nam a2200841 i 4500 |
| 001 - CONTROL NUMBER |
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7464101 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200413152921.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
160513s2016 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781608451135 |
| Qualifying information |
ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Canceled/invalid ISBN |
9781608451128 |
| Qualifying information |
print |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.2200/S00705ED1V01Y201602COV007 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(CaBNVSL)swl00406487 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)949811480 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
CaBNVSL |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TA1634 |
| Item number |
.B273 2016 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.37 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Barnard, Kobus., |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Computational methods for integrating vision and language / |
| Statement of responsibility, etc. |
Kobus Barnard. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : |
| Name of producer, publisher, distributor, manufacturer |
Morgan & Claypool, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2016. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 PDF (xvi, 211 pages) : |
| Other physical details |
illustrations. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
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rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
electronic |
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isbdmedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
online resource |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Synthesis lectures on computer vision, |
| International Standard Serial Number |
2153-1064 ; |
| Volume/sequential designation |
# 7 |
| 538 ## - SYSTEM DETAILS NOTE |
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| System details note |
Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE |
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| System details note |
System requirements: Adobe Acrobat Reader. |
| 500 ## - GENERAL NOTE |
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| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references (pages 155-210). |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1. Introduction -- 1.1 Redundant, complementary, and orthogonal multimodal data -- 1.1.1 Multimodal mutual information -- 1.1.2 Complementary multimodal information -- 1.2 Computational tasks -- 1.2.1 Multimodal translation -- 1.2.2 Integrating complementary multimodal data and cross modal disambiguation -- 1.2.3 Grounding language with sensory data -- 1.3 Multimodal modeling -- 1.3.1 Discriminative methods -- 1.4 Mutimodal inference, applications to computational tasks -- 1.4.1 Region labeling with a concept model -- 1.4.2 Cross-modal disambiguation, region labeling with image keywordS -- 1.4.3 Cross-modal disambiguation, word sense disambiguation with images -- 1.5 Learning from redundant representations in loosely labeled multimodal data -- 1.5.1 Resolving region-label correspondence ambiguity -- 1.5.2 Data variation and semantic grouping -- 1.5.3 Simultaneously learning models and reducing correspondence ambiguity -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
2. The semantics of images and associated text -- 2.1 Lessons from image search -- 2.1.1 Content-based image retrieval (CBIR) -- 2.2 Images and text as evidence about the world -- 2.3 Affective attributes of images and video -- 2.3.1 Emotion induction from images and video -- 2.3.2 Inferring emotion in people depicted in images and videos -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
3. Sources of data for linking visual and linguistic information -- 3.1 WordNet for building semantic visual-linguistic data sets -- 3.2 Visual data with a single objective label -- 3.3 Visual data with a single subjective label -- 3.4 Visual data with keywords or object labels -- 3.4.1 Localized labels -- 3.4.2 Semantic segmentations with labels -- 3.5 Visual data with descriptions -- 3.6 Image data with questions and answers -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
4. Extracting and representing visual information -- 4.1 Low-level features -- 4.1.1 Color -- 4.1.2 Edges -- 4.1.3 Texture -- 4.1.4 Characterizing neighborhoods using histograms of oriented gradients -- 4.2 Segmentation for low-level spatial grouping -- 4.3 Representation of regions and patches -- 4.3.1 Visual word representations -- 4.4 Mid-level representations for images -- 4.4.1 Artificial neural network representations -- 4.5 Object category recognition and detection -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
5. Text and speech processing -- 5.1 Text associated with audiovisual data -- 5.2 Text embedded within visual data -- 5.3 Basic natural language processing -- 5.4 Word sense disambiguation -- 5.5 Online lexical resource for vision and language integration -- 5.5.1 WordNet -- 5.5.2 Representing words by vectors -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
6. Modeling images and keywords -- 6.1 Scene semantic-keywords for entire images -- 6.2 Localized semantics-keywords for regions -- 6.3 Generative models with independent multi-modal concepts -- 6.3.1 Notational preliminaries -- 6.3.2 Semantic concepts with multi-model evidence -- 6.3.3 Joint modeling of images and keywords (PWRM and IRCM) -- 6.3.4 Inferring image keywords and region labels -- 6.3.5 Learning multi-modal concept models from loosely labeled data -- 6.3.6 Evaluation of region labeling and image annotation -- 6.4 Translation models -- 6.4.1 Notational preliminaries (continuing 6.3.1) -- 6.4.2 A simple region translation model (RTM) -- 6.4.3 Visual translation models for broadcast video -- 6.4.4 A word translation model (WTM) -- 6.4.5 Supervised multiclass labeling (SML) -- 6.4.6 Discriminative models for translation -- 6.5 Image clustering and interdependencies among concepts -- 6.5.1 Region concepts with image categories (CIRCM) -- 6.5.2 Latent dirichlet allocation (LDA) -- 6.5.3 Multiclass supervised LDA (sLDA) with annotations -- 6.6 Segmentation, region grouping, and spatial context -- 6.6.1 Notational preliminaries (continuing 6.3.1 and 6.4.1) -- 6.6.2 Random fields for representing image semantics -- 6.6.3 Joint learning of translation and spatial relationships -- 6.6.4 Multistage learning and inference -- 6.6.5 Dense CRFs for general context -- 6.6.6 Dense CRFs for multiple pairwise relationships -- 6.6.7 Multiscale CRF (mCRF) -- 6.6.8 Relative location prior with CRFs -- 6.6.9 Encoding spatial patterns into the unary potentials with texture-layout features -- 6.6.10 Discriminative region labeling with spatial and scene information -- 6.6.11 Holistic integration of appearance, object detection, and scene type -- 6.7 Image annotation without localization -- 6.7.1 Nonparametric generative models -- 6.7.2 Label propagation -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
7. Beyond simple nouns -- 7.1 Reasoning with proper nouns -- 7.1.1 Names and faces in the news -- 7.1.2 Linking action verbs to pose-who is doing what? -- 7.1.3 Learning structured appearance for named objects -- 7.2 Learning and using adjectives and attributes -- 7.2.1 Learning visual attributes for color names -- 7.2.2 Learning complex visual attributes for specific domains -- 7.2.3 Inferring emotional attributes for images -- 7.2.4 Inferring emotional attributes for video clips -- 7.2.5 Sentiment analysis in consumer photographs and videos -- 7.2.6 Extracting aesthetic attributes for images -- 7.2.7 Addressing subjectivity -- 7.3 Noun-noun relationships-spatial prepositions and comparative adjectives -- 7.3.1 Learning about preposition use in natural language -- 7.4 Linking visual data to verbs -- 7.5 Vision helping language understanding -- 7.5.1 Using vision to improve word sense disambiguation -- 7.5.2 Using vision to improve coreference resolution -- 7.5.3 Discovering visual-semantic senses -- 7.6 Using associated text to improve visual understanding -- 7.6.1 Using captions to improve semantic image parsing (cardinality and prepositions) -- 7.7 Using world knowledge from text sources for visual understanding -- 7.7.1 Seeing what cannot be seen? -- 7.7.2 World knowledge for training large-scale fine-grained visual models -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
8. Sequential structure -- 8.1 Automated image and video captioning -- 8.1.1 Captioning by reusing existing sentences and fragments -- 8.1.2 Captioning using templates, schemas, or simple grammars -- 8.1.3 Captioning video using storyline models -- 8.1.4 Captioning with learned sentence generators -- 8.2 Aligning sentences with images and video -- 8.3 Automatic illustration of text documents -- 8.4 Visual question and answering -- |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
A. Additional definitions and derivations -- Basic definitions from probability and information theory -- Additional considerations for multimodal evidence for a concept -- Loosely labeled vs. strongly labeled data -- Pedantic derivation of equation (6.13) -- Derivation of the EM equations for the image region concept model (IRCM) -- Bibliography -- Author's biography. |
| 506 1# - RESTRICTIONS ON ACCESS NOTE |
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| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Modeling data from visual and linguistic modalities together creates opportunities for better understanding of both, and supports many useful applications. Examples of dual visual-linguistic data includes images with keywords, video with narrative, and figures in documents. We consider two key task-driven themes: translating from one modality to another (e.g., inferring annotations for images) and understanding the data using all modalities, where one modality can help disambiguate information in another. The multiple modalities can either be essentially semantically redundant (e.g., keywords provided by a person looking at the image), or largely complementary (e.g., meta data such as the camera used). Redundancy and complementarity are two endpoints of a scale, and we observe that good performance on translation requires some redundancy, and that joint inference is most useful where some information is complementary. Computational methods discussed are broadly organized into ones for simple keywords, ones going beyond keywords toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of semantics, going from words about the scene taken as whole, to words that apply to specific parts of the scene, to relationships between parts. Methods going beyond keywords are organized by the linguistic roles that are learned, exploited, or generated. These include proper nouns, adjectives, spatial and comparative prepositions, and verbs. More recent developments in dealing with sequential structure include automated captioning of scenes and video, alignment of video and text, and automated answering of questions about scenes depicted in images. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
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| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF title page (viewed on May 13, 2016). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer vision |
| General subdivision |
Mathematical models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Information visualization. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Closed captioning |
| General subdivision |
Technological innovations. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Keyword searching |
| General subdivision |
Technological innovations. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Natural language processing (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Multimodal user interfaces (Computer systems) |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
vision |
| 653 ## - INDEX TERM--UNCONTROLLED |
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| Uncontrolled term |
language |
| 653 ## - INDEX TERM--UNCONTROLLED |
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| Uncontrolled term |
loosely labeled data |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
correspondence ambiguity |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
auto-annotation |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
region labeling |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
multimodal translation |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
cross-modal disambiguation |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
image captioning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
video captioning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
affective visual attributes |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
aligning visual and linguistic data |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
auto-illustration |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
visual question answering |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781608451128 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on computer vision ; |
| Volume/sequential designation |
# 7. |
| International Standard Serial Number |
2153-1064 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
http://ieeexplore.ieee.org/servlet/opac?bknumber=7464101 |
