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Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations (Synthesis Lectures on Information Concepts, Retrieval, and Services) book cover by Lei Zhu, Jingjing Li, Weili Guan
Paperback

Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations (Synthesis Lectures on Information Concepts, Retrieval, and Services)

by Lei Zhu, Jingjing Li and Weili Guan

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Paperback · 199 pages Springer International Publishing · 2024

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About the book

Multi-modal Hash Learning

This book systemically presents key concepts of multi-modal hashing technology, recent advances on large-scale efficient multimedia search and recommendation, and recent achievements in multimedia indexing technology.  With the explosive growth of multimedia contents, multimedia retrieval is currently facing unprecedented challenges in both storage cost and retrieval speed. The multi-modal hashing technique can project high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding.  The authors introduce the categorization of existing multi-modal hashing methods according to various metrics and datasets. The authors also collect recent multi-modal hashing techniques and describe the motivation, objective formulations, and optimization steps for context-aware hashing methods based on the tag-semantics transfer.  




Details

Publisher
Springer International Publishing
Authors
Lei Zhu, Jingjing Li, Weili Guan
Format
Paperback
Published
Aug 23, 2024
ISBN-13
9783031372933
Pages
199
Printing
Printed on demand
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