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    Enhanced Discrete Multi-modal Hashing: more constraints yet less time to learn

    Chen, Y. and Zhang, H. and Tian, Z. and Wang, J. and Zhang, Dell and Li, Xuelong (2022) Enhanced Discrete Multi-modal Hashing: more constraints yet less time to learn. IEEE Transactions on Knowledge and Data Engineering (TKDE) 34 (3), pp. 1177-1190. ISSN 1041-4347.

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    Due to the exponential growth of multimedia data, multi-modal hashing as a promising technique to make cross-view retrieval scalable is attracting more and more attention. However, most of the existing multi-modal hashing methods either divide the learning process unnaturally into two separate stages or treat the discrete optimization problem simplistically as a continuous one, which leads to suboptimal results. Recently, a few discrete multi-modal hashing methods that try to address such issues have emerged, but they still ignore several important discrete constraints (such as the balance and decorrelation of hash bits). In this paper, we overcome those limitations by proposing a novel method named "Enhanced Discrete Multi-modal Hashing (EDMH)" which learns binary codes and hashing functions simultaneously from the pairwise similarity matrix of data, under the aforementioned discrete constraints. Although the model of EDMH looks a lot more complex than the other models for multi-modal hashing, we are actually able to develop a fast iterative learning algorithm for it, since the subproblems of its optimization all have closed-form solutions after introducing two auxiliary variables. Our experimental results on three real-world datasets have revealed the usefulness of those previously ignored discrete constraints and demonstrated that EDMH not only performs much better than state-of-the-art competitors according to several retrieval metrics but also runs much faster than most of them.


    Item Type: Article
    Additional Information: (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): learning to hash, discrete optimization, semantics alignment, cross-view retrieval
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for
    Depositing User: Dell Zhang
    Date Deposited: 04 May 2020 10:30
    Last Modified: 09 Aug 2023 12:48


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