Multi-modal Hash Learning - Zhu, Lei
- Format: Relié Voir le descriptif
Vous en avez un à vendre ?
Vendez-le-vôtreSoyez informé(e) par e-mail dès l'arrivée de cet article
Créer une alerte prix
- Payez directement sur Rakuten (CB, PayPal, 4xCB...)
- Récupérez le produit directement chez le vendeur
- Rakuten vous rembourse en cas de problème
Gratuit et sans engagement
Félicitations !
Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !
TROUVER UN MAGASIN
Retour
Avis sur Multi - Modal Hash Learning de Zhu, Lei Format Relié - Livre Informatique
0 avis sur Multi - Modal Hash Learning de Zhu, Lei Format Relié - Livre Informatique
Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.
Présentation Multi - Modal Hash Learning de Zhu, Lei Format Relié
- Livre Informatique
Résumé :
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.
Biographie:
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....
Sommaire:
1 Introduction.- 2 Context-aware Hashing.- 3 Cross-modal Hashing.- 4 Composite Multi-modal Hashing.- 5 Multi-modal Discrete Collaborative Filtering.- 6 Research Frontiers.