Data Management for Multimedia Retrieval

A wide-ranging textbook covering data structures and algorithms for storing, indexing, clustering, classifying, and accessing common multimedia data representations.

K. Selçuk Candan (Author), Maria Luisa Sapino (Author)

9780521887397, Cambridge University Press

Hardback, published 31 May 2010

500 pages, 195 b/w illus. 15 tables
26 x 18.5 x 2.8 cm, 1.04 kg

This is a very timely book which fills a long felt gap of a comprehensive textbook possessing depth in the Multimedia Information Systems area. With a distinctively database systems perspective, it provides a refreshingly detailed and balanced treatment of the necessary multimedia content processing fundamentals. This book can serve as the reference text for senior undergraduate and graduate courses in Multimedia Information Systems. It will also be an excellent self-contained take-off point for beginning researchers in Multimedia Information Retrieval and Multimedia Databases. Moreover, Multimedia Signal Processing researchers can use it to gain a solid understanding of the Database Systems issues."
Mohan S.  Kankanhalli, School of Computing, National University of Singapore

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.

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.

Subject Areas: Databases & the Web [UNN], Database design & theory [UNA], Algorithms & data structures [UMB], Graphical & digital media applications [UG]