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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.
Foreword | p. xi |
List of Figures | p. xiii |
List of Tables | p. xvii |
List of Contributors | p. xix |
Introduction | p. 1 |
Use Case Scenarios | p. 7 |
Photo Use Case | p. 8 |
Motivating Examples | p. 8 |
Semantic Description of Photos Today | p. 9 |
Services We Need for Photo Collections | p. 10 |
Music Use Case | p. 10 |
Semantic Description of Music Assets | p. 11 |
Music Recommendation and Discovery | p. 12 |
Management of Personal Music Collections | p. 13 |
Annotation in Professional Media Production and Archiving | p. 14 |
Motivating Examples | p. 15 |
Requirements for Content Annotation | p. 17 |
Discussion | p. 18 |
Acknowledgements | p. 19 |
Canonical Processes of Semantically Annotated Media Production | p. 21 |
Canonical Processes | p. 22 |
Premeditate | p. 23 |
Create Media Asset | p. 23 |
Annotate | p. 23 |
Package | p. 24 |
Query | p. 24 |
Construct Message | p. 25 |
Organize | p. 25 |
Publish | p. 26 |
Distribute | p. 26 |
Example Systems | p. 27 |
CeWe Color Photo Book | p. 27 |
SenseCam | p. 29 |
Conclusion and Future Work | p. 33 |
Feature Extraction for Multimedia Analysis | p. 35 |
Low-Level Feature Extraction | p. 36 |
What Are Relevant Low-Level Features? | p. 36 |
Visual Descriptors | p. 36 |
Audio Descriptors | p. 45 |
Feature Fusion and Multi-modality | p. 54 |
Feature Normalization | p. 54 |
Homogeneous Fusion | p. 55 |
Cross-modal Fusion | p. 56 |
Conclusion | p. 58 |
Machine Learning Techniques for Multimedia Analysis | p. 59 |
Feature Selection | p. 61 |
Selection Criteria | p. 61 |
Subset Search | p. 62 |
Feature Ranking | p. 63 |
A Supervised Algorithm Example | p. 63 |
Classification | p. 65 |
Historical Classification Algorithms | p. 65 |
Kernel Methods | p. 67 |
Classifying Sequences | p. 71 |
Biologically Inspired Machine Learning Techniques | p. 73 |
Classifier Fusion | p. 75 |
Introduction | p. 75 |
Non-trainable Combiners | p. 75 |
Trainable Combiners | p. 76 |
Combination of Weak Classifiers | p. 77 |
Evidence Theory | p. 78 |
Consensual Clustering | p. 78 |
Classifier Fusion Properties | p. 80 |
Conclusion | p. 80 |
Semantic Web Basics | p. 81 |
The Semantic Web | p. 82 |
RDF | p. 83 |
RDF Graphs | p. 86 |
Named Graphs | p. 87 |
RDF Semantics | p. 88 |
RDF Schema | p. 90 |
Data Models | p. 93 |
Linked Data Principles | p. 94 |
Dereferencing Using Basic Web Look-up | p. 95 |
Dereferencing Using HTTP 303 Redirects | p. 95 |
Development Practicalities | p. 96 |
Data Stores | p. 97 |
Toolkits | p. 97 |
Semantic Web Languages | p. 99 |
The Need for Ontologies on the Semantic Web | p. 100 |
Representing Ontological Knowledge Using OWL | p. 100 |
OWL Constructs and OWL Syntax | p. 100 |
The Formal Semantics of OWL and its Different Layers | p. 102 |
Reasoning Tasks | p. 106 |
OWL Flavors | p. 107 |
Beyond OWL | p. 107 |
A Language to Represent Simple Conceptual Vocabularies: SKOS | p. 108 |
Ontologies versus Knowledge Organization Systems | p. 108 |
Representing Concept Schemes Using SKOS | p. 109 |
Characterizing Concepts beyond SKOS | p. 111 |
Using SKOS Concept Schemes on the Semantic Web | p. 112 |
Querying on the Semantic Web | p. 113 |
Syntax | p. 113 |
Semantics | p. 118 |
Default Negation in SPARQL | p. 123 |
Well-Formed Queries | p. 124 |
Querying for Multimedia Metadata | p. 124 |
Partitioning Datasets | p. 126 |
Related Work | p. 127 |
Multimedia Metadata Standards | p. 129 |
Selected Standards | p. 130 |
MPEG-7 | p. 130 |
EBU P_Meta | p. 132 |
SMPTE Metadata Standards | p. 133 |
Dublin Core | p. 133 |
TV-Anytime | p. 134 |
METS and VRA | p. 134 |
MPEG-21 | p. 135 |
XMP, IPTC in XMP | p. 135 |
EXIF | p. 136 |
DIG35 | p. 137 |
ID3/MP3 | p. 137 |
NewsML G2 and rNews | p. 138 |
W3C Ontology for Media Resources | p. 138 |
EBUCore | p. 139 |
Comparison | p. 140 |
Conclusion | p. 143 |
The Core Ontology for Multimedia | p. 145 |
Introduction | p. 145 |
A Multimedia Presentation for Granddad | p. 146 |
Related Work | p. 149 |
Requirements for Designing a Multimedia Ontology | p. 150 |
A Formal Representation for MPEG-7 | p. 150 |
DOLCE as Modeling Basis | p. 151 |
Multimedia Patterns | p. 151 |
Basic Patterns | p. 155 |
Comparison with Requirements | p. 157 |
Granddad's Presentation Explained by COMM | p. 157 |
Lessons Learned | p. 159 |
Conclusion | p. 160 |
Knowledge-Driven Segmentation and Classification | p. 163 |
Related Work | p. 164 |
Semantic Image Segmentation | p. 165 |
Graph Representation of an Image | p. 165 |
Image Graph Initialization | p. 165 |
Semantic Region Growing | p. 167 |
Using Contextual Knowledge to Aid Visual Analysis | p. 170 |
Contextual Knowledge Formulation | p. 170 |
Contextual Relevance | p. 173 |
Spatial Context and Optimization | p. 177 |
Introduction | p. 177 |
Low-Level Visual Information Processing | p. 177 |
Initial Region-Concept Association | p. 178 |
Final Region-Concept Association | p. 179 |
Conclusions | p. 181 |
Reasoning for Multimedia Analysis | p. 183 |
Fuzzy DL Reasoning | p. 184 |
The Fuzzy DLf-SKLM | p. 184 |
The Tableaux Algorithm | p. 185 |
The FiRE Fuzzy Reasoning Engine | p. 187 |
Spatial Features for Image Region Labeling | p. 192 |
Fuzzy Constraint Satisfaction Problems | p. 192 |
Exploiting Spatial Features Using Fuzzy Constraint Reasoning | p. 193 |
Fuzzy Rule Based Reasoning Engine | p. 196 |
Reasoning over Resources Complementary to Audiovisual Streams | p. 201 |
Multi-Modal Analysis for Content Structuring and Event Detection | p. 205 |
Moving Beyond Shots for Extracting Semantics | p. 206 |
A Multi-Modal Approach | p. 207 |
Case Studies | p. 207 |
Case Study 1: Field Sports | p. 208 |
Content Structuring | p. 208 |
Concept Detection Leveraging Complementary Text Sources | p. 213 |
Case Study 2: Fictional Content | p. 214 |
Content Structuring | p. 215 |
Concept Detection Leveraging Audio Description | p. 219 |
Conclusions and Future Work | p. 221 |
Multimedia Annotation Tools | p. 223 |
State of the Art | p. 224 |
SVAT: Professional Video Annotation | p. 225 |
User Interface | p. 225 |
Semantic Annotation | p. 228 |
KAT: Semi-automatic, Semantic Annotation of Multimedia Content | p. 229 |
History | p. 231 |
Architecture | p. 232 |
Default Plugins | p. 234 |
Using COMM as an Underlying Model: Issues and Solutions | p. 234 |
Semi-automatic Annotation: An Example | p. 237 |
Conclusions | p. 239 |
Information Organization Issues in Multimedia Retrieval Using Low-Level Features | p. 241 |
Efficient Multimedia Indexing Structures | p. 242 |
An Efficient Access Structure for Multimedia Data | p. 243 |
Experimental Results | p. 245 |
Conclusion | p. 249 |
Feature Term Based Index | p. 249 |
Feature Terms | p. 250 |
Feature Term Distribution | p. 251 |
Feature Term Extraction | p. 252 |
Feature Dimension Selection | p. 253 |
Collection Representation and Retrieval System | p. 254 |
Experiment | p. 256 |
Conclusion | p. 258 |
Conclusion and Future Trends | p. 259 |
Acknowledgement | p. 259 |
The Role of Explicit Semantics in Search and Browsing | p. 261 |
Basic Search Terminology | p. 261 |
Analysis of Semantic Search | p. 262 |
Query Construction | p. 263 |
Search Algorithm | p. 265 |
Presentation of Results | p. 267 |
Survey Summary | p. 269 |
Use Case A: Keyword Search in ClioPatria | p. 270 |
Query Construction | p. 270 |
Search Algorithm | p. 270 |
Result Visualization and Organization | p. 273 |
Use Case B: Faceted Browsing in ClioPatria | p. 274 |
Query Construction | p. 274 |
Search Algorithm | p. 276 |
Result Visualization and Organization | p. 276 |
Conclusions | p. 277 |
Conclusion | p. 279 |
References | p. 281 |
Author Index | p. 301 |
Subject Index | p. 303 |
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