Multimedia Semantics

Informationen zum Autor 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. Klappentext 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 (http://www.multimediasemantics.com/)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. Zusammenfassung 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": t...

Klappentext

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 (http://www.multimediasemantics.com/) 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.

Inhalt

Foreword xi List of Figures xiii List of Tables xvii List of Contributors xix 1 Introduction 1 Raphaël 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¡êljko 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, Gaël 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, Gaël 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 Fusi…