K. Selcuk Candan

Data Management for Multimedia Retrieval (eBook, PDF)

• Format: PDF

Produktbeschreibung

Multimedia data require specialised management techniques because the representations of colour, time, semantic concepts, and other underlying information can be drastically different from one another. This textbook on multimedia data management techniques gives a unified perspective on retrieval efficiency and effectiveness. It provides a comprehensive treatment, from basic to advanced concepts, that will be useful to readers of different levels, from advanced undergraduate and graduate students to researchers and to professionals. After introducing models for multimedia data (images, video, audio, text, and web) and for their features, such as colour, texture, shape, and time, the book presents data structures and algorithms that help store, index, cluster, classify, and access common data representations. The authors also introduce techniques, such as relevance feedback and collaborative filtering, for bridging the 'semantic gap' and present the applications of these to emerging topics, including web and social networking.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 31. Mai 2010
• Englisch
• ISBN-13: 9780511795749
• Artikelnr.: 38206245

Autorenporträt

K. Selçuk Candan is a Professor of Computer Science and Engineering at Arizona State University. He received his Ph.D. in 1997 from the University of Maryland at College Park. Candan has authored more than 120 conference and journal articles, 9 patents, and many book chapters and, among his other scientific positions, has served as program chair for ACM Multimedia Conference '08, the Int. Conference on Image and Video Retrieval (CIVR'10), and as an organizing committee member for ACM SIG Management of Data Conference (SIGMOD'06). Since 2005, he has also served as an editorial board member for the Very Large Databases (VLDB) journal.

Inhaltsangabe

1. Introduction: multimedia applications and data management requirements
2. Models for multimedia data
3. Common representations of multimedia features
4. Feature quality and independence: why and how?
5. Indexing, search, and retrieval of sequences
6. Indexing, search, retrieval of graphs and trees
7. Indexing, search, and retrieval of vectors
8. Clustering techniques
9. Classification
10. Ranked retrieval
11. Evaluation of retrieval
12. User relevance feedback and collaborative filtering.