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    Strongly Constrained Discrete Hashing

    Chen, Y. and Tian, Z. and Zhang, H. and Wang, J. and Zhang, Dell (2019) Strongly Constrained Discrete Hashing. IEEE Transactions on Image Processing 29 , ISSN 1057-7149. (In Press)

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    Abstract

    Learning to hash is a fundamental technique widely used in large-scale image retrieval. Most existing methods for learning to hash address the involved discrete optimization problem by the continuous relaxation of the binary constraint, which usually leads to large quantization errors and consequently suboptimal binary codes. A few discrete hashing methods have emerged recently. However, they either completely ignore some useful constraints (specifically the balance and decorrelation of hash bits) or just turn those constraints into regularizers that would make the optimization easier but less accurate. In this paper, we propose a novel supervised hashing method named Strongly Constrained Discrete Hashing (SCDH) which overcomes such limitations. It can learn the binary codes for all examples in the training set, and meanwhile obtain a hash function for unseen samples with the above-mentioned constraints preserved. Although the model of SCDH is fairly sophisticated, we are able to find closed-form solutions to all of its optimization subproblems and thus design an efficient algorithm that converges quickly. In addition, we extend SCDH to a kernelized version SCDH_K. Our experiments on three large benchmark datasets have demonstrated that not only can SCDH and SCDH_K achieve substantially higher MAP scores than state-of-the-art baselines, but they run much faster than those that are also supervised.

    Metadata

    Item Type: Article
    Additional Information: (c) 2019 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, image retrieval
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Dell Zhang
    Date Deposited: 06 Jan 2020 11:16
    Last Modified: 13 Jan 2020 18:28
    URI: http://eprints.bbk.ac.uk/id/eprint/30409

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