Data Management for Multimedia Retrieval

This wide-ranging textbook on multimedia data management gives a unified perspective on retrieval efficiency and effectiveness. It presents data structures and algorithms for storing, indexing, clustering, classifying, and accessing common multimedia data representations, plus related techniques such as relevance feedback and collaborative filtering.

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.