Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations - Rilegato

Zhu, Lei; Li, Jingjing; Guan, Weili

 
9783031372902: Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations
Rilegato
ISBN 10: 3031372905 ISBN 13: 9783031372902
Casa editrice: Springer-Nature New York Inc, 2023

Sinossi

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.  


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Informazioni sull?autore

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.

Dalla quarta di copertina

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.  

  • Reveals the key concepts and recent advancements of multi-modal hashing and multimedia indexing technology
  • Introduces standard multimedia retrieval datasets, evaluation metrics for multi-modal hashing, and the unprecedented challenges of both storage cost and retrieval speed
  • Discusses how the multi-modal hashing technique can project high-dimensional data into compact binary hash codes

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer-Nature New York Inc
Data di pubblicazione
2023
Lingua
Inglese
ISBN 10 
3031372905
ISBN 13 
9783031372902
Rilegatura
Copertina rigida
Numero di pagine
224
Contatto del produttore
non disponibile
Persona responsabile
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Altre edizioni note dello stesso titolo

9783031372926: Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations

Edizione in evidenza

ISBN 10:  3031372921 ISBN 13:  9783031372926
Casa editrice: Springer, 2023
Brossura