03931nam a22005655i 4500 978-3-031-37291-9 DE-He213 20251030110810.0 cr nn 008mamaa 230804s2024 sz | s |||| 0|eng d 9783031372919 978-3-031-37291-9 10.1007/978-3-031-37291-9 doi QA75.5-76.95 UNH bicssc UND bicssc COM030000 bisacsh UNH thema UND thema Zhu, Lei. author. aut http://id.loc.gov/vocabulary/relators/aut 65567 Multi-modal Hash Learning [electronic resource] : Efficient Multimedia Retrieval and Recommendations / by Lei Zhu, Jingjing Li, Weili Guan. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XXII, 199 p. 51 illus., 50 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Synthesis Lectures on Information Concepts, Retrieval, and Services, 1947-9468 1 Introduction -- 2 Context-aware Hashing -- 3 Cross-modal Hashing -- 4 Composite Multi-modal Hashing -- 5 Multi-modal Discrete Collaborative Filtering -- 6 Research Frontiers. . 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. Information storage and retrieval systems. 65568 Data mining. 6941 Multimedia systems. 65569 Information Storage and Retrieval. 65570 Data Mining and Knowledge Discovery. 65571 Multimedia Information Systems. 65572 Li, Jingjing. author. aut http://id.loc.gov/vocabulary/relators/aut 65573 Guan, Weili. author. aut http://id.loc.gov/vocabulary/relators/aut 65574 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031372902 Printed edition: 9783031372926 Printed edition: 9783031372933 Synthesis Lectures on Information Concepts, Retrieval, and Services, 1947-9468 65575 https://doi.org/10.1007/978-3-031-37291-9 ZDB-2-SXSC 56228 56228