Enhanced Discrete Multi-modal Hashing: More Constraints yet Less Time to Learn (Extended Abstract)

This paper proposes a novel method, Enhanced Discrete Multi-modal Hashing (EDMH), which learns binary codes and hash functions simultaneously from the pairwise similarity matrix of data for large-scale cross-view retrieval. EDMH distinguishes itself from existing methods by considering not just the...

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Bibliographic Details
Main Authors: Chen, Yong, Zhang, Hui, Tian, Zhibao, Wang, Jun, Zhang, Dell, Li, Xuelong
Format: Conference Proceeding
Language:English
Subjects:
Binary codes
Closed-form solutions
Computational efficiency
cross-view retrieval
Data engineering
Decorrelation
discrete optimization
Hash functions
Iterative algorithms
learning to hash
semantics alignment
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Summary:This paper proposes a novel method, Enhanced Discrete Multi-modal Hashing (EDMH), which learns binary codes and hash functions simultaneously from the pairwise similarity matrix of data for large-scale cross-view retrieval. EDMH distinguishes itself from existing methods by considering not just the binarization constraint but also the balance and decorrelation constraints. Although those additional discrete constraints make the optimization problem of EDMH look a lot more complicated, we are actually able to develop a fast iterative learning algorithm in the alternating optimization framework for it, as after introducing a couple of auxiliary variables each subproblem of optimization turns out to have closed-form solutions. It has been confirmed by extensive experiments that EDMH can consistently deliver better retrieval performances than state-of-the-art MH methods at lower computational costs.
ISSN:2375-026X
DOI:10.1109/ICDE55515.2023.00355