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Multi-modal Hash Learning [electronic resource] : Efficient Multimedia Retrieval and Recommendations / by Lei Zhu, Jingjing Li, Weili Guan.

By: Contributor(s): Material type: TextSeries: Synthesis Lectures on Information Concepts, Retrieval, and ServicesPublisher: Cham : Springer International Publishing : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XXII, 199 p. 51 illus., 50 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031372919
Subject(s):
Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • QA75.5-76.95
Online resources:
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. .
In: Springer Nature eBookSummary: 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.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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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.

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