Data Management for Multimedia Retrieval

K. Selcuk Candan, Maria Luisa Sapino

Published by Cambridge University Press
ISBN 10: 0521887399 / ISBN 13: 9780521887397
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BRAND NEW, Data Management for Multimedia Retrieval, K. Selcuk Candan, Maria Luisa Sapino, 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. Bookseller Inventory #

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Synopsis: Multimedia data require specialized management techniques because the representations of color, time, semantic concepts, and other underlying information can be drastically different from one another. The user's subjective judgment can also have significant impact on what data or features are relevant in a given context. These factors affect both the performance of the retrieval algorithms and their effectiveness. This textbook on multimedia data management techniques offers 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 color, 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.

Book Description: This comprehensive textbook on multimedia data management for advanced undergraduate and graduate students, researchers and professionals offers 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.

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Title: Data Management for Multimedia Retrieval
Publisher: Cambridge University Press
Binding: Hardback
Book Condition: new

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K. Selçuk;Sapino Candan
Published by Cambridge University Press (2010)
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Book Description Cambridge University Press, 2010. Gebundene Ausgabe. Book Condition: Gebraucht. Gebraucht - Gut Neuware, Importqualität, Sofortversand - A wide-ranging textbook covering data structures and algorithms for storing, indexing, clustering, classifying, and accessing common multimedia data representations. 500 pp. Englisch. Bookseller Inventory # INF1000133775

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Book Description Cambridge University Press. Hardback. Book Condition: new. BRAND NEW, Data Management for Multimedia Retrieval, K. Selcuk Candan, Maria Luisa Sapino, 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. Bookseller Inventory # B9780521887397

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2010. Hardback. Book Condition: New. Language: English . Brand New Book. 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. Bookseller Inventory # AAA9780521887397

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2010. Hardback. Book Condition: New. Language: English . Brand New Book. 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. Bookseller Inventory # AAA9780521887397

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2010. Hardback. Book Condition: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. 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. Bookseller Inventory # BTE9780521887397

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