Multimedia Semantics Metadata, Analysis and Interaction

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2011-08-22
  • Publisher: Wiley-Blackwell

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Multimedia Semantics: Metadata, Analysis and Interaction explains, collects and reports on the latest research results that aim at narrowing the so-called multimedia ;Semantic Gap ;; the large disparity between descriptions of multimedia content that can be computed automatically and the richness and subjectivity of semantics in user queries and human interpretations of audiovisual media. Addressing the grand challenge posed by the ;Semantic Gap ; requires a multi-disciplinary approach (computer science, cognitive science, web science, etc.) and this is reflected in recent research in this area. This book is thus targeting an interdisciplinary community and in particular the Multimedia and the Semantic Web communities. A growing community of researchers is now pursuing joint approaches in various high-profile projects across the globe. However, it remains difficult for both sides of the divide to communicate and learn from each other. This book aims to provide both the fundamental knowledge and the latest state-of-the-art results from both communities with the goal of making the knowledge of one community understandable to the other.   This book is divided into four parts: Part A (Use Cases Scenarios, Canonical Processes of Semantically Annotated Media Production, Feature Extraction for Multimedia Analysis, Machine Learning Techniques for Multimedia Analysis, Semantic Web Basics, Semantic Web Languages), Part B (Multimedia Metadata Standards, COMM: A Core Ontology for Multimedia, Multimedia Ontology and Domain-Specific Ontologies), Part C (Knowledge Driven Segmentation and Classification, Reasoning for Multimedia Analysis, Large Scale Detection of Semantic Concepts in Multimedia) and Part D (Multimedia Annotation Tools, Multimedia Retrieval, Multimedia Browsing.)

Author Biography

Dr. Raphaël Troncy, Centre for Mathematics and Computer Science, Netherlands
Raphaël Troncy obtained his Master's thesis with honours in computer science at the University Joseph Fourier of Grenoble, France. He received his PhD with honours in 2004. His research interests include Semantic Web and Multimedia Technologies, Knowledge Representation, Ontology Modeling and Alignment. Raphaël Troncy is an expert in audio visual metadata and in combining existing metadata standards (such as MPEG-7) with current Semantic Web technologies.

Dr. Benoit Huet, Institut EURECOM, France
Benoit Huet received his BSc degree in computer science and engineering from the Ecole Superieure de Technologie Electrique (Groupe ESIEE, France) in 1992. In 1993, he was awarded the MSc degree in Artificial Intelligence from the University of Westminster (UK) with distinction. He received his PhD degree in Computer Science from the University of York (UK). His research interests include computer vision, content-based retrieval, multimedia data mining and indexing (still and/or moving images) and pattern recognition.

Simon Schenk, University of Koblenz-Landau, Germany
Simon Schenk is a research and teaching assistant at the Information Systems and Semantic Web Group of University of Koblenz-Landau.Simon is working towards his PhD degree under the supervision of Professor Dr. Steffen Staab. Previously, he has worked as a consultant for Capgemini. Schenk studied at NORDAKADEMIE University of Applied Sciences, Germany and Karlstads Universitet, Sweden and received his diploma in Computer Science and Business Management from NORDAKADEMIE in 2004.

Table of Contents

Forewordp. xi
List of Figuresp. xiii
List of Tablesp. xvii
List of Contributorsp. xix
Introductionp. 1
Use Case Scenariosp. 7
Photo Use Casep. 8
Motivating Examplesp. 8
Semantic Description of Photos Todayp. 9
Services We Need for Photo Collectionsp. 10
Music Use Casep. 10
Semantic Description of Music Assetsp. 11
Music Recommendation and Discoveryp. 12
Management of Personal Music Collectionsp. 13
Annotation in Professional Media Production and Archivingp. 14
Motivating Examplesp. 15
Requirements for Content Annotationp. 17
Discussionp. 18
Acknowledgementsp. 19
Canonical Processes of Semantically Annotated Media Productionp. 21
Canonical Processesp. 22
Premeditatep. 23
Create Media Assetp. 23
Annotatep. 23
Packagep. 24
Queryp. 24
Construct Messagep. 25
Organizep. 25
Publishp. 26
Distributep. 26
Example Systemsp. 27
CeWe Color Photo Bookp. 27
SenseCamp. 29
Conclusion and Future Workp. 33
Feature Extraction for Multimedia Analysisp. 35
Low-Level Feature Extractionp. 36
What Are Relevant Low-Level Features?p. 36
Visual Descriptorsp. 36
Audio Descriptorsp. 45
Feature Fusion and Multi-modalityp. 54
Feature Normalizationp. 54
Homogeneous Fusionp. 55
Cross-modal Fusionp. 56
Conclusionp. 58
Machine Learning Techniques for Multimedia Analysisp. 59
Feature Selectionp. 61
Selection Criteriap. 61
Subset Searchp. 62
Feature Rankingp. 63
A Supervised Algorithm Examplep. 63
Classificationp. 65
Historical Classification Algorithmsp. 65
Kernel Methodsp. 67
Classifying Sequencesp. 71
Biologically Inspired Machine Learning Techniquesp. 73
Classifier Fusionp. 75
Introductionp. 75
Non-trainable Combinersp. 75
Trainable Combinersp. 76
Combination of Weak Classifiersp. 77
Evidence Theoryp. 78
Consensual Clusteringp. 78
Classifier Fusion Propertiesp. 80
Conclusionp. 80
Semantic Web Basicsp. 81
The Semantic Webp. 82
RDFp. 83
RDF Graphsp. 86
Named Graphsp. 87
RDF Semanticsp. 88
RDF Schemap. 90
Data Modelsp. 93
Linked Data Principlesp. 94
Dereferencing Using Basic Web Look-upp. 95
Dereferencing Using HTTP 303 Redirectsp. 95
Development Practicalitiesp. 96
Data Storesp. 97
Toolkitsp. 97
Semantic Web Languagesp. 99
The Need for Ontologies on the Semantic Webp. 100
Representing Ontological Knowledge Using OWLp. 100
OWL Constructs and OWL Syntaxp. 100
The Formal Semantics of OWL and its Different Layersp. 102
Reasoning Tasksp. 106
OWL Flavorsp. 107
Beyond OWLp. 107
A Language to Represent Simple Conceptual Vocabularies: SKOSp. 108
Ontologies versus Knowledge Organization Systemsp. 108
Representing Concept Schemes Using SKOSp. 109
Characterizing Concepts beyond SKOSp. 111
Using SKOS Concept Schemes on the Semantic Webp. 112
Querying on the Semantic Webp. 113
Syntaxp. 113
Semanticsp. 118
Default Negation in SPARQLp. 123
Well-Formed Queriesp. 124
Querying for Multimedia Metadatap. 124
Partitioning Datasetsp. 126
Related Workp. 127
Multimedia Metadata Standardsp. 129
Selected Standardsp. 130
MPEG-7p. 130
EBU P_Metap. 132
SMPTE Metadata Standardsp. 133
Dublin Corep. 133
TV-Anytimep. 134
METS and VRAp. 134
MPEG-21p. 135
XMP, IPTC in XMPp. 135
EXIFp. 136
DIG35p. 137
ID3/MP3p. 137
NewsML G2 and rNewsp. 138
W3C Ontology for Media Resourcesp. 138
EBUCorep. 139
Comparisonp. 140
Conclusionp. 143
The Core Ontology for Multimediap. 145
Introductionp. 145
A Multimedia Presentation for Granddadp. 146
Related Workp. 149
Requirements for Designing a Multimedia Ontologyp. 150
A Formal Representation for MPEG-7p. 150
DOLCE as Modeling Basisp. 151
Multimedia Patternsp. 151
Basic Patternsp. 155
Comparison with Requirementsp. 157
Granddad's Presentation Explained by COMMp. 157
Lessons Learnedp. 159
Conclusionp. 160
Knowledge-Driven Segmentation and Classificationp. 163
Related Workp. 164
Semantic Image Segmentationp. 165
Graph Representation of an Imagep. 165
Image Graph Initializationp. 165
Semantic Region Growingp. 167
Using Contextual Knowledge to Aid Visual Analysisp. 170
Contextual Knowledge Formulationp. 170
Contextual Relevancep. 173
Spatial Context and Optimizationp. 177
Introductionp. 177
Low-Level Visual Information Processingp. 177
Initial Region-Concept Associationp. 178
Final Region-Concept Associationp. 179
Conclusionsp. 181
Reasoning for Multimedia Analysisp. 183
Fuzzy DL Reasoningp. 184
The Fuzzy DLf-SKLMp. 184
The Tableaux Algorithmp. 185
The FiRE Fuzzy Reasoning Enginep. 187
Spatial Features for Image Region Labelingp. 192
Fuzzy Constraint Satisfaction Problemsp. 192
Exploiting Spatial Features Using Fuzzy Constraint Reasoningp. 193
Fuzzy Rule Based Reasoning Enginep. 196
Reasoning over Resources Complementary to Audiovisual Streamsp. 201
Multi-Modal Analysis for Content Structuring and Event Detectionp. 205
Moving Beyond Shots for Extracting Semanticsp. 206
A Multi-Modal Approachp. 207
Case Studiesp. 207
Case Study 1: Field Sportsp. 208
Content Structuringp. 208
Concept Detection Leveraging Complementary Text Sourcesp. 213
Case Study 2: Fictional Contentp. 214
Content Structuringp. 215
Concept Detection Leveraging Audio Descriptionp. 219
Conclusions and Future Workp. 221
Multimedia Annotation Toolsp. 223
State of the Artp. 224
SVAT: Professional Video Annotationp. 225
User Interfacep. 225
Semantic Annotationp. 228
KAT: Semi-automatic, Semantic Annotation of Multimedia Contentp. 229
Historyp. 231
Architecturep. 232
Default Pluginsp. 234
Using COMM as an Underlying Model: Issues and Solutionsp. 234
Semi-automatic Annotation: An Examplep. 237
Conclusionsp. 239
Information Organization Issues in Multimedia Retrieval Using Low-Level Featuresp. 241
Efficient Multimedia Indexing Structuresp. 242
An Efficient Access Structure for Multimedia Datap. 243
Experimental Resultsp. 245
Conclusionp. 249
Feature Term Based Indexp. 249
Feature Termsp. 250
Feature Term Distributionp. 251
Feature Term Extractionp. 252
Feature Dimension Selectionp. 253
Collection Representation and Retrieval Systemp. 254
Experimentp. 256
Conclusionp. 258
Conclusion and Future Trendsp. 259
Acknowledgementp. 259
The Role of Explicit Semantics in Search and Browsingp. 261
Basic Search Terminologyp. 261
Analysis of Semantic Searchp. 262
Query Constructionp. 263
Search Algorithmp. 265
Presentation of Resultsp. 267
Survey Summaryp. 269
Use Case A: Keyword Search in ClioPatriap. 270
Query Constructionp. 270
Search Algorithmp. 270
Result Visualization and Organizationp. 273
Use Case B: Faceted Browsing in ClioPatriap. 274
Query Constructionp. 274
Search Algorithmp. 276
Result Visualization and Organizationp. 276
Conclusionsp. 277
Conclusionp. 279
Referencesp. 281
Author Indexp. 301
Subject Indexp. 303
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