Based on more than 10 years of teaching experience, Blanken and his coeditors have assembled all the topics that should be covered in advanced undergraduate or graduate courses on multimedia retrieval and multimedia databases. The single chapters of this textbook explain the general architecture of multimedia information retrieval systems and cover various metadata languages such as Dublin Core, RDF, or MPEG. The authors emphasize high-level features and show how these are used in mathematical models to support the retrieval process. For each chapter, there’s detail on further reading, and additional exercises and teaching material is available online.

Everything you ever wanted to know about multimedia retrieval and management. This comprehensive book offers a full picture of the cutting-edge technologies necessary for a profound introduction to the field. Leading experts also cover a broad range of practical applications.

This comprehensive text/reference examines in depth the synergy between multimedia content analysis, personalization, and next-generation networking. The book demonstrates how this integration can result in robust, personalized services that provide users with an improved multimedia-centric quality of experience. Each chapter offers a practical step-by-step walkthrough for a variety of concepts, components and technologies relating to the development of applications and services. Topics and features: introduces the fundamentals of social media retrieval, presenting the most important areas of research in this domain; examines the important topic of multimedia tagging in social environments, including geo-tagging; discusses issues of personalization and privacy in social media; reviews advances in encoding, compression and network architectures for the exchange of social media information; describes a range of applications related to social media.

"This book offers solutions to the challenges of storage and manipulation of a variety of media types providing data placement techniques, scheduling methods, caching techniques and emerging characteristics of multimedia information. Academicians, students, professionals and practitioners in the multimedia industry will benefit from this ground-breaking publication"--Provided by publisher.

Supporting users in their resource discovery mission when hunting for multimedia material is not a technological indexing problem alone. We look at interactiveways of engaging with repositories through browsing and relevance feedback, roping in geographical context, and providing visual summaries for videos. The book concludes with an overview of state-of-the-art research projects in the area of multimedia information retrieval, which gives an indication of the research and development trends and, thereby, a glimpse of the future world.

"Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions. The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, Machine Learning, Large-scale Data Mining, Database, and Multimedia Information Retrieval. Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.

This book is an extended collection of contributions that wereoriginally subm- ted to the 1st International Workshop on Adaptive Multimedia Retrieval (AMR 2003), which was organized as part of the 26th German Conference on Arti?cial Intelligence (KI 2003),and held during September 15–18,2003at the University of Hamburg, Germany. Motivated by the overall success of the workshop – as revealed by the stimulating atmosphere during the workshop and the number of very interested and active participants – we ?nally decided to edit a book based on revised papers that were initially submitted to the workshop. Furthermore, we invited some more introductory contributions in order to be able to provide a conclusive book on current topics in the area of adaptive multimedia retrieval systems. We hope that we were able to put together a stimulating collection of articles for the interested reader. We like to thank the organizationcommittee of the 26th German Conference on Arti?cial Intelligence (KI 2003) for providing the setting and the admin- trative support in realizing this workshop as part of their program. Especially, we like to thank Christopher Habel for promoting the workshop as part of the conference program and Andreas Gun ̈ ther for his kind support throughout the organization process.