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Multimedia semantics : metadata, analysis and interaction / Rapha�eel Troncy, Benoit Huet, Simon Schenk.

By: Troncy, Raphael [author.].
Contributor(s): Huet, Benoit | Schenk, Simon | IEEE Xplore (Online Service) [distributor.] | Wiley [publisher.].
Material type: materialTypeLabelBookPublisher: Chichester, West Sussex, U.K. : Wiley, 2011Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2011]Description: 1 PDF (xxii, 305 pages) : illustrations, maps.Content type: text Media type: electronic Carrier type: online resourceISBN: 9781119970231.Subject(s): Multimedia systems | Semantic computing | Information retrieval | Database searching | MetadataGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.7 Online resources: Abstract with links to resource Also available in print.
Contents:
Foreword xi -- List of Figures xiii -- List of Tables xvii -- List of Contributors xix -- 1 Introduction 1 / Rapha�el Troncy, Benoit Huet and Simon Schenk -- 2 Use Case Scenarios 7 / Werner Bailer, Susanne Boll, Oscar Celma, Michael Hausenblas and Yves Raimond -- 2.1 Photo Use Case 8 -- 2.1.1 Motivating Examples 8 -- 2.1.2 Semantic Description of Photos Today 9 -- 2.1.3 Services We Need for Photo Collections 10 -- 2.2 Music Use Case 10 -- 2.2.1 Semantic Description of Music Assets 11 -- 2.2.2 Music Recommendation and Discovery 12 -- 2.2.3 Management of Personal Music Collections 13 -- 2.3 Annotation in Professional Media Production and Archiving 14 -- 2.3.1 Motivating Examples 15 -- 2.3.2 Requirements for Content Annotation 17 -- 2.4 Discussion 18 -- Acknowledgements 19 -- 3 Canonical Processes of Semantically Annotated Media Production 21 / Lynda Hardman, Z��eljko Obrenovic� and Frank Nack -- 3.1 Canonical Processes 22 -- 3.1.1 Premeditate 23 -- 3.1.2 Create Media Asset 23 -- 3.1.3 Annotate 23 -- 3.1.4 Package 24 -- 3.1.5 Query 24 -- 3.1.6 Construct Message 25 -- 3.1.7 Organize 25 -- 3.1.8 Publish 26 -- 3.1.9 Distribute 26 -- 3.2 Example Systems 27 -- 3.2.1 CeWe Color Photo Book 27 -- 3.2.2 SenseCam 29 -- 3.3 Conclusion and Future Work 33 -- 4 Feature Extraction for Multimedia Analysis 35 / Rachid Benmokhtar, Benoit Huet, Ga�el Richard and Slim Essid -- 4.1 Low-Level Feature Extraction 36 -- 4.1.1 What Are Relevant Low-Level Features? 36 -- 4.1.2 Visual Descriptors 36 -- 4.1.3 Audio Descriptors 45 -- 4.2 Feature Fusion and Multi-modality 54 -- 4.2.1 Feature Normalization 54 -- 4.2.2 Homogeneous Fusion 55 -- 4.2.3 Cross-modal Fusion 56 -- 4.3 Conclusion 58 -- 5 Machine Learning Techniques for Multimedia Analysis 59 / Slim Essid, Marine Campedel, Ga�el Richard, Tomas Piatrik, Rachid Benmokhtar and Benoit Huet -- 5.1 Feature Selection 61 -- 5.1.1 Selection Criteria 61 -- 5.1.2 Subset Search 62 -- 5.1.3 Feature Ranking 63 -- 5.1.4 A Supervised Algorithm Example 63 -- 5.2 Classification 65.
5.2.1 Historical Classification Algorithms 65 -- 5.2.2 Kernel Methods 67 -- 5.2.3 Classifying Sequences 71 -- 5.2.4 Biologically Inspired Machine Learning Techniques 73 -- 5.3 Classifier Fusion 75 -- 5.3.1 Introduction 75 -- 5.3.2 Non-trainable Combiners 75 -- 5.3.3 Trainable Combiners 76 -- 5.3.4 Combination of Weak Classifiers 77 -- 5.3.5 Evidence Theory 78 -- 5.3.6 Consensual Clustering 78 -- 5.3.7 Classifier Fusion Properties 80 -- 5.4 Conclusion 80 -- 6 Semantic Web Basics 81 / Eyal Oren and Simon Schenk -- 6.1 The Semantic Web 82 -- 6.2 RDF 83 -- 6.2.1 RDF Graphs 86 -- 6.2.2 Named Graphs 87 -- 6.2.3 RDF Semantics 88 -- 6.3 RDF Schema 90 -- 6.4 Data Models 93 -- 6.5 Linked Data Principles 94 -- 6.5.1 Dereferencing Using Basic Web Look-up 95 -- 6.5.2 Dereferencing Using HTTP 303 Redirects 95 -- 6.6 Development Practicalities 96 -- 6.6.1 Data Stores 97 -- 6.6.2 Toolkits 97 -- 7 Semantic Web Languages 99 / Antoine Isaac, Simon Schenk and Ansgar Scherp -- 7.1 The Need for Ontologies on the Semantic Web 100 -- 7.2 Representing Ontological Knowledge Using OWL 100 -- 7.2.1 OWL Constructs and OWL Syntax 100 -- 7.2.2 The Formal Semantics of OWL and its Different Layers 102 -- 7.2.3 Reasoning Tasks 106 -- 7.2.4 OWL Flavors 107 -- 7.2.5 Beyond OWL 107 -- 7.3 A Language to Represent Simple Conceptual Vocabularies: SKOS 108 -- 7.3.1 Ontologies versus Knowledge Organization Systems 108 -- 7.3.2 Representing Concept Schemes Using SKOS 109 -- 7.3.3 Characterizing Concepts beyond SKOS 111 -- 7.3.4 Using SKOS Concept Schemes on the Semantic Web 112 -- 7.4 Querying on the Semantic Web 113 -- 7.4.1 Syntax 113 -- 7.4.2 Semantics 118 -- 7.4.3 Default Negation in SPARQL 123 -- 7.4.4 Well-Formed Queries 124 -- 7.4.5 Querying for Multimedia Metadata 124 -- 7.4.6 Partitioning Datasets 126 -- 7.4.7 Related Work 127 -- 8 Multimedia Metadata Standards 129 / Peter Schallauer, Werner Bailer, Rapha�el Troncy and Florian Kaiser -- 8.1 Selected Standards 130 -- 8.1.1 MPEG-7 130 -- 8.1.2 EBU P_Meta 132.
8.1.3 SMPTE Metadata Standards 133 -- 8.1.4 Dublin Core 133 -- 8.1.5 TV-Anytime 134 -- 8.1.6 METS and VRA 134 -- 8.1.7 MPEG-21 135 -- 8.1.8 XMP, IPTC in XMP 135 -- 8.1.9 EXIF 136 -- 8.1.10 DIG35 137 -- 8.1.11 ID3/MP3 137 -- 8.1.12 NewsML G2 and rNews 138 -- 8.1.13 W3C Ontology for Media Resources 138 -- 8.1.14 EBUCore 139 -- 8.2 Comparison 140 -- 8.3 Conclusion 143 -- 9 The Core Ontology for Multimedia 145 / Thomas Franz, Rapha�el Troncy and Miroslav Vacura -- 9.1 Introduction 145 -- 9.2 A Multimedia Presentation for Granddad 146 -- 9.3 Related Work 149 -- 9.4 Requirements for Designing a Multimedia Ontology 150 -- 9.5 A Formal Representation for MPEG-7 150 -- 9.5.1 DOLCE as Modeling Basis 151 -- 9.5.2 Multimedia Patterns 151 -- 9.5.3 Basic Patterns 155 -- 9.5.4 Comparison with Requirements 157 -- 9.6 Granddad's Presentation Explained by COMM 157 -- 9.7 Lessons Learned 159 -- 9.8 Conclusion 160 -- 10 Knowledge-Driven Segmentation and Classification 163 / Thanos Athanasiadis, Phivos Mylonas, Georgios Th. Papadopoulos, Vasileios Mezaris, Yannis Avrithis, Ioannis Kompatsiaris and Michael G. Strintzis -- 10.1 Related Work 164 -- 10.2 Semantic Image Segmentation 165 -- 10.2.1 Graph Representation of an Image 165 -- 10.2.2 Image Graph Initialization 165 -- 10.2.3 Semantic Region Growing 167 -- 10.3 Using Contextual Knowledge to Aid Visual Analysis 170 -- 10.3.1 Contextual Knowledge Formulation 170 -- 10.3.2 Contextual Relevance 173 -- 10.4 Spatial Context and Optimization 177 -- 10.4.1 Introduction 177 -- 10.4.2 Low-Level Visual Information Processing 177 -- 10.4.3 Initial Region-Concept Association 178 -- 10.4.4 Final Region-Concept Association 179 -- 10.5 Conclusions 181 -- 11 Reasoning for Multimedia Analysis 183 / Nikolaos Simou, Giorgos Stoilos, Carsten Saathoff, Jan Nemrava, Vojt�ech Sv�atek, Petr Berka and Vassilis Tzouvaras -- 11.1 Fuzzy DL Reasoning 184 -- 11.1.1 The Fuzzy DL f-SHIN 184 -- 11.1.2 The Tableaux Algorithm 185 -- 11.1.3 The FiRE Fuzzy Reasoning Engine 187.
11.2 Spatial Features for Image Region Labeling 192 -- 11.2.1 Fuzzy Constraint Satisfaction Problems 192 -- 11.2.2 Exploiting Spatial Features Using Fuzzy -- Constraint Reasoning 193 -- 11.3 Fuzzy Rule Based Reasoning Engine 196 -- 11.4 Reasoning over Resources Complementary to Audiovisual Streams 201 -- 12 Multi-Modal Analysis for Content Structuring and Event Detection 205 / Noel E. O'Connor, David A. Sadlier, Bart Lehane, Andrew Salway, Jan Nemrava and Paul Buitelaar -- 12.1 Moving Beyond Shots for Extracting Semantics 206 -- 12.2 A Multi-Modal Approach 207 -- 12.3 Case Studies 207 -- 12.4 Case Study 1: Field Sports 208 -- 12.4.1 Content Structuring 208 -- 12.4.2 Concept Detection Leveraging Complementary Text Sources 213 -- 12.5 Case Study 2: Fictional Content 214 -- 12.5.1 Content Structuring 215 -- 12.5.2 Concept Detection Leveraging Audio Description 219 -- 12.6 Conclusions and Future Work 221 -- 13 Multimedia Annotation Tools 223 / Carsten Saathoff, Krishna Chandramouli, Werner Bailer, Peter Schallauer and Rapha�el Troncy -- 13.1 State of the Art 224 -- 13.2 SVAT: Professional Video Annotation 225 -- 13.2.1 User Interface 225 -- 13.2.2 Semantic Annotation 228 -- 13.3 KAT: Semi-automatic, Semantic Annotation of Multimedia Content 229 -- 13.3.1 History 231 -- 13.3.2 Architecture 232 -- 13.3.3 Default Plugins 234 -- 13.3.4 Using COMM as an Underlying Model: Issues and Solutions 234 -- 13.3.5 Semi-automatic Annotation: An Example 237 -- 13.4 Conclusions 239 -- 14 Information Organization Issues in Multimedia Retrieval Using Low-Level Features 241 / Frank Hopfgartner, Reede Ren, Thierry Urruty and Joemon M. Jose -- 14.1 Efficient Multimedia Indexing Structures 242 -- 14.1.1 An Efficient Access Structure for Multimedia Data 243 -- 14.1.2 Experimental Results 245 -- 14.1.3 Conclusion 249 -- 14.2 Feature Term Based Index 249 -- 14.2.1 Feature Terms 250 -- 14.2.2 Feature Term Distribution 251 -- 14.2.3 Feature Term Extraction 252 -- 14.2.4 Feature Dimension Selection 253.
14.2.5 Collection Representation and Retrieval System 254 -- 14.2.6 Experiment 256 -- 14.2.7 Conclusion 258 -- 14.3 Conclusion and Future Trends 259 -- Acknowledgement 259 -- 15 The Role of Explicit Semantics in Search and Browsing 261 / Michiel Hildebrand, Jacco van Ossenbruggen and Lynda Hardman -- 15.1 Basic Search Terminology 261 -- 15.2 Analysis of Semantic Search 262 -- 15.2.1 Query Construction 263 -- 15.2.2 Search Algorithm 265 -- 15.2.3 Presentation of Results 267 -- 15.2.4 Survey Summary 269 -- 15.3 Use Case A: Keyword Search in ClioPatria 270 -- 15.3.1 Query Construction 270 -- 15.3.2 Search Algorithm 270 -- 15.3.3 Result Visualization and Organization 273 -- 15.4 Use Case B: Faceted Browsing in ClioPatria 274 -- 15.4.1 Query Construction 274 -- 15.4.2 Search Algorithm 276 -- 15.4.3 Result Visualization and Organization 276 -- 15.5 Conclusions 277 -- 16 Conclusion 279 / Rapha�el Troncy, Benoit Huet and Simon Schenk -- References 281 -- Author Index 301 -- Subject Index 303.
Summary: In this book, the authors present the latest research results in the multimedia and semantic web communities, bridging the "Semantic Gap" This book 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, computer vision and signal processing, cognitive science, web science, etc.) and this is reflected in recent research in this area. In addition, the book targets an interdisciplinary community, and in particular the Multimedia and the Semantic Web communities. Finally, the authors 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 available to the other. Key Features: * Presents state-of-the art research results in multimedia semantics: multimedia analysis, metadata standards and multimedia knowledge representation, semantic interaction with multimedia * Contains real industrial problems exemplified by user case scenarios * Offers an insight into various standardisation bodies including W3C, IPTC and ISO MPEG * Contains contributions from academic and industrial communities from Europe, USA and Asia * Includes an accompanying website containing user cases, datasets, and software mentioned in the book, as well as links to the K-Space NoE and the SMaRT society web sites (<a href="http://www.multimediasemantics.com/">http://www.multimediasemantics.com/</a>) This book will be a valuable reference for academic and industry researchers /practitioners in multimedia, computational intelligence and computer science fields. Graduate students, project leaders, and consultants will also find this book of interest.
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Includes bibliographical references (p. [281]-299) and index.

Foreword xi -- List of Figures xiii -- List of Tables xvii -- List of Contributors xix -- 1 Introduction 1 / Rapha�el Troncy, Benoit Huet and Simon Schenk -- 2 Use Case Scenarios 7 / Werner Bailer, Susanne Boll, Oscar Celma, Michael Hausenblas and Yves Raimond -- 2.1 Photo Use Case 8 -- 2.1.1 Motivating Examples 8 -- 2.1.2 Semantic Description of Photos Today 9 -- 2.1.3 Services We Need for Photo Collections 10 -- 2.2 Music Use Case 10 -- 2.2.1 Semantic Description of Music Assets 11 -- 2.2.2 Music Recommendation and Discovery 12 -- 2.2.3 Management of Personal Music Collections 13 -- 2.3 Annotation in Professional Media Production and Archiving 14 -- 2.3.1 Motivating Examples 15 -- 2.3.2 Requirements for Content Annotation 17 -- 2.4 Discussion 18 -- Acknowledgements 19 -- 3 Canonical Processes of Semantically Annotated Media Production 21 / Lynda Hardman, Z��eljko Obrenovic� and Frank Nack -- 3.1 Canonical Processes 22 -- 3.1.1 Premeditate 23 -- 3.1.2 Create Media Asset 23 -- 3.1.3 Annotate 23 -- 3.1.4 Package 24 -- 3.1.5 Query 24 -- 3.1.6 Construct Message 25 -- 3.1.7 Organize 25 -- 3.1.8 Publish 26 -- 3.1.9 Distribute 26 -- 3.2 Example Systems 27 -- 3.2.1 CeWe Color Photo Book 27 -- 3.2.2 SenseCam 29 -- 3.3 Conclusion and Future Work 33 -- 4 Feature Extraction for Multimedia Analysis 35 / Rachid Benmokhtar, Benoit Huet, Ga�el Richard and Slim Essid -- 4.1 Low-Level Feature Extraction 36 -- 4.1.1 What Are Relevant Low-Level Features? 36 -- 4.1.2 Visual Descriptors 36 -- 4.1.3 Audio Descriptors 45 -- 4.2 Feature Fusion and Multi-modality 54 -- 4.2.1 Feature Normalization 54 -- 4.2.2 Homogeneous Fusion 55 -- 4.2.3 Cross-modal Fusion 56 -- 4.3 Conclusion 58 -- 5 Machine Learning Techniques for Multimedia Analysis 59 / Slim Essid, Marine Campedel, Ga�el Richard, Tomas Piatrik, Rachid Benmokhtar and Benoit Huet -- 5.1 Feature Selection 61 -- 5.1.1 Selection Criteria 61 -- 5.1.2 Subset Search 62 -- 5.1.3 Feature Ranking 63 -- 5.1.4 A Supervised Algorithm Example 63 -- 5.2 Classification 65.

5.2.1 Historical Classification Algorithms 65 -- 5.2.2 Kernel Methods 67 -- 5.2.3 Classifying Sequences 71 -- 5.2.4 Biologically Inspired Machine Learning Techniques 73 -- 5.3 Classifier Fusion 75 -- 5.3.1 Introduction 75 -- 5.3.2 Non-trainable Combiners 75 -- 5.3.3 Trainable Combiners 76 -- 5.3.4 Combination of Weak Classifiers 77 -- 5.3.5 Evidence Theory 78 -- 5.3.6 Consensual Clustering 78 -- 5.3.7 Classifier Fusion Properties 80 -- 5.4 Conclusion 80 -- 6 Semantic Web Basics 81 / Eyal Oren and Simon Schenk -- 6.1 The Semantic Web 82 -- 6.2 RDF 83 -- 6.2.1 RDF Graphs 86 -- 6.2.2 Named Graphs 87 -- 6.2.3 RDF Semantics 88 -- 6.3 RDF Schema 90 -- 6.4 Data Models 93 -- 6.5 Linked Data Principles 94 -- 6.5.1 Dereferencing Using Basic Web Look-up 95 -- 6.5.2 Dereferencing Using HTTP 303 Redirects 95 -- 6.6 Development Practicalities 96 -- 6.6.1 Data Stores 97 -- 6.6.2 Toolkits 97 -- 7 Semantic Web Languages 99 / Antoine Isaac, Simon Schenk and Ansgar Scherp -- 7.1 The Need for Ontologies on the Semantic Web 100 -- 7.2 Representing Ontological Knowledge Using OWL 100 -- 7.2.1 OWL Constructs and OWL Syntax 100 -- 7.2.2 The Formal Semantics of OWL and its Different Layers 102 -- 7.2.3 Reasoning Tasks 106 -- 7.2.4 OWL Flavors 107 -- 7.2.5 Beyond OWL 107 -- 7.3 A Language to Represent Simple Conceptual Vocabularies: SKOS 108 -- 7.3.1 Ontologies versus Knowledge Organization Systems 108 -- 7.3.2 Representing Concept Schemes Using SKOS 109 -- 7.3.3 Characterizing Concepts beyond SKOS 111 -- 7.3.4 Using SKOS Concept Schemes on the Semantic Web 112 -- 7.4 Querying on the Semantic Web 113 -- 7.4.1 Syntax 113 -- 7.4.2 Semantics 118 -- 7.4.3 Default Negation in SPARQL 123 -- 7.4.4 Well-Formed Queries 124 -- 7.4.5 Querying for Multimedia Metadata 124 -- 7.4.6 Partitioning Datasets 126 -- 7.4.7 Related Work 127 -- 8 Multimedia Metadata Standards 129 / Peter Schallauer, Werner Bailer, Rapha�el Troncy and Florian Kaiser -- 8.1 Selected Standards 130 -- 8.1.1 MPEG-7 130 -- 8.1.2 EBU P_Meta 132.

8.1.3 SMPTE Metadata Standards 133 -- 8.1.4 Dublin Core 133 -- 8.1.5 TV-Anytime 134 -- 8.1.6 METS and VRA 134 -- 8.1.7 MPEG-21 135 -- 8.1.8 XMP, IPTC in XMP 135 -- 8.1.9 EXIF 136 -- 8.1.10 DIG35 137 -- 8.1.11 ID3/MP3 137 -- 8.1.12 NewsML G2 and rNews 138 -- 8.1.13 W3C Ontology for Media Resources 138 -- 8.1.14 EBUCore 139 -- 8.2 Comparison 140 -- 8.3 Conclusion 143 -- 9 The Core Ontology for Multimedia 145 / Thomas Franz, Rapha�el Troncy and Miroslav Vacura -- 9.1 Introduction 145 -- 9.2 A Multimedia Presentation for Granddad 146 -- 9.3 Related Work 149 -- 9.4 Requirements for Designing a Multimedia Ontology 150 -- 9.5 A Formal Representation for MPEG-7 150 -- 9.5.1 DOLCE as Modeling Basis 151 -- 9.5.2 Multimedia Patterns 151 -- 9.5.3 Basic Patterns 155 -- 9.5.4 Comparison with Requirements 157 -- 9.6 Granddad's Presentation Explained by COMM 157 -- 9.7 Lessons Learned 159 -- 9.8 Conclusion 160 -- 10 Knowledge-Driven Segmentation and Classification 163 / Thanos Athanasiadis, Phivos Mylonas, Georgios Th. Papadopoulos, Vasileios Mezaris, Yannis Avrithis, Ioannis Kompatsiaris and Michael G. Strintzis -- 10.1 Related Work 164 -- 10.2 Semantic Image Segmentation 165 -- 10.2.1 Graph Representation of an Image 165 -- 10.2.2 Image Graph Initialization 165 -- 10.2.3 Semantic Region Growing 167 -- 10.3 Using Contextual Knowledge to Aid Visual Analysis 170 -- 10.3.1 Contextual Knowledge Formulation 170 -- 10.3.2 Contextual Relevance 173 -- 10.4 Spatial Context and Optimization 177 -- 10.4.1 Introduction 177 -- 10.4.2 Low-Level Visual Information Processing 177 -- 10.4.3 Initial Region-Concept Association 178 -- 10.4.4 Final Region-Concept Association 179 -- 10.5 Conclusions 181 -- 11 Reasoning for Multimedia Analysis 183 / Nikolaos Simou, Giorgos Stoilos, Carsten Saathoff, Jan Nemrava, Vojt�ech Sv�atek, Petr Berka and Vassilis Tzouvaras -- 11.1 Fuzzy DL Reasoning 184 -- 11.1.1 The Fuzzy DL f-SHIN 184 -- 11.1.2 The Tableaux Algorithm 185 -- 11.1.3 The FiRE Fuzzy Reasoning Engine 187.

11.2 Spatial Features for Image Region Labeling 192 -- 11.2.1 Fuzzy Constraint Satisfaction Problems 192 -- 11.2.2 Exploiting Spatial Features Using Fuzzy -- Constraint Reasoning 193 -- 11.3 Fuzzy Rule Based Reasoning Engine 196 -- 11.4 Reasoning over Resources Complementary to Audiovisual Streams 201 -- 12 Multi-Modal Analysis for Content Structuring and Event Detection 205 / Noel E. O'Connor, David A. Sadlier, Bart Lehane, Andrew Salway, Jan Nemrava and Paul Buitelaar -- 12.1 Moving Beyond Shots for Extracting Semantics 206 -- 12.2 A Multi-Modal Approach 207 -- 12.3 Case Studies 207 -- 12.4 Case Study 1: Field Sports 208 -- 12.4.1 Content Structuring 208 -- 12.4.2 Concept Detection Leveraging Complementary Text Sources 213 -- 12.5 Case Study 2: Fictional Content 214 -- 12.5.1 Content Structuring 215 -- 12.5.2 Concept Detection Leveraging Audio Description 219 -- 12.6 Conclusions and Future Work 221 -- 13 Multimedia Annotation Tools 223 / Carsten Saathoff, Krishna Chandramouli, Werner Bailer, Peter Schallauer and Rapha�el Troncy -- 13.1 State of the Art 224 -- 13.2 SVAT: Professional Video Annotation 225 -- 13.2.1 User Interface 225 -- 13.2.2 Semantic Annotation 228 -- 13.3 KAT: Semi-automatic, Semantic Annotation of Multimedia Content 229 -- 13.3.1 History 231 -- 13.3.2 Architecture 232 -- 13.3.3 Default Plugins 234 -- 13.3.4 Using COMM as an Underlying Model: Issues and Solutions 234 -- 13.3.5 Semi-automatic Annotation: An Example 237 -- 13.4 Conclusions 239 -- 14 Information Organization Issues in Multimedia Retrieval Using Low-Level Features 241 / Frank Hopfgartner, Reede Ren, Thierry Urruty and Joemon M. Jose -- 14.1 Efficient Multimedia Indexing Structures 242 -- 14.1.1 An Efficient Access Structure for Multimedia Data 243 -- 14.1.2 Experimental Results 245 -- 14.1.3 Conclusion 249 -- 14.2 Feature Term Based Index 249 -- 14.2.1 Feature Terms 250 -- 14.2.2 Feature Term Distribution 251 -- 14.2.3 Feature Term Extraction 252 -- 14.2.4 Feature Dimension Selection 253.

14.2.5 Collection Representation and Retrieval System 254 -- 14.2.6 Experiment 256 -- 14.2.7 Conclusion 258 -- 14.3 Conclusion and Future Trends 259 -- Acknowledgement 259 -- 15 The Role of Explicit Semantics in Search and Browsing 261 / Michiel Hildebrand, Jacco van Ossenbruggen and Lynda Hardman -- 15.1 Basic Search Terminology 261 -- 15.2 Analysis of Semantic Search 262 -- 15.2.1 Query Construction 263 -- 15.2.2 Search Algorithm 265 -- 15.2.3 Presentation of Results 267 -- 15.2.4 Survey Summary 269 -- 15.3 Use Case A: Keyword Search in ClioPatria 270 -- 15.3.1 Query Construction 270 -- 15.3.2 Search Algorithm 270 -- 15.3.3 Result Visualization and Organization 273 -- 15.4 Use Case B: Faceted Browsing in ClioPatria 274 -- 15.4.1 Query Construction 274 -- 15.4.2 Search Algorithm 276 -- 15.4.3 Result Visualization and Organization 276 -- 15.5 Conclusions 277 -- 16 Conclusion 279 / Rapha�el Troncy, Benoit Huet and Simon Schenk -- References 281 -- Author Index 301 -- Subject Index 303.

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In this book, the authors present the latest research results in the multimedia and semantic web communities, bridging the "Semantic Gap" This book 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, computer vision and signal processing, cognitive science, web science, etc.) and this is reflected in recent research in this area. In addition, the book targets an interdisciplinary community, and in particular the Multimedia and the Semantic Web communities. Finally, the authors 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 available to the other. Key Features: * Presents state-of-the art research results in multimedia semantics: multimedia analysis, metadata standards and multimedia knowledge representation, semantic interaction with multimedia * Contains real industrial problems exemplified by user case scenarios * Offers an insight into various standardisation bodies including W3C, IPTC and ISO MPEG * Contains contributions from academic and industrial communities from Europe, USA and Asia * Includes an accompanying website containing user cases, datasets, and software mentioned in the book, as well as links to the K-Space NoE and the SMaRT society web sites (<a href="http://www.multimediasemantics.com/">http://www.multimediasemantics.com/</a>) This book will be a valuable reference for academic and industry researchers /practitioners in multimedia, computational intelligence and computer science fields. Graduate students, project leaders, and consultants will also find this book of interest.

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