Fr. 55.50

Multi-modal Hash Learning - Efficient Multimedia Retrieval and Recommendations

English · Hardback

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Description

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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.  

List of contents

1 Introduction.- 2 Context-aware Hashing.- 3 Cross-modal Hashing.- 4 Composite Multi-modal Hashing.- 5 Multi-modal Discrete Collaborative Filtering.- 6 Research Frontiers. 

About the author










Lei Zhu, Ph.D. is a Professor in the School of Information Science and Engineering, Shandong Normal University. He received his B.Eng. and Ph.D. degrees from Wuhan University of Technology and Huazhong University Science and Technology, respectively. He was a Research Fellow at the University of Queensland (2016-2017). His research interests include large-scale multimedia content analysis and retrieval. Jingjing Li, Ph.D, is a Professor in the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). He received his B.Eng., M.Sc. and Ph.D. degrees from UESTC. His research interests include domain adaptation and zero-shot learning. 

Weili Guan received a master degree from National University of Singapore. After that, she joined Hewlett Packard Enterprise in Singapore as a Software Engineer and worked there for several years.  She is currently a PhD student with the Faculty of Information Technology, Monash University (Clayton Campus), Australia. Her research interests are multimedia computing and information retrieval. She has authored or co-authored more than 30 papers at first-tier conferences and journals, such as ACM MM, SIGIR, and IEEE TIP.


Product details

Authors Weili Guan, Jingjing Li, Lei Zhu
Publisher Springer, Berlin
 
Languages English
Product format Hardback
Released 21.11.2023
 
EAN 9783031372902
ISBN 978-3-0-3137290-2
No. of pages 199
Dimensions 168 mm x 16 mm x 240 mm
Illustrations XXII, 199 p. 51 illus., 50 illus. in color.
Series Synthesis Lectures on Information Concepts, Retrieval, and Services
Subject Natural sciences, medicine, IT, technology > IT, data processing > IT

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