BIROn - Birkbeck Institutional Research Online

    Unsupervised Hashing based on the Recovery of Subspace Structures

    Tian, Z. and Zhang, H. and Chen, Y. and Zhang, Dell (2020) Unsupervised Hashing based on the Recovery of Subspace Structures. Pattern Recognition 103 , p. 107261. ISSN 0031-3203.

    [img]
    Preview
    Text
    PR-D-19-00651R3_accepted.pdf - Author's Accepted Manuscript
    Available under License Creative Commons Attribution Non-commercial No Derivatives.

    Download (4MB) | Preview

    Abstract

    Unsupervised semantic hashing should in principle keep the semantics among samples consistent with the intrinsic geometric structures of the dataset. In this paper, we propose a novel multiple stage unsupervised hashing method, named "Unsupervised Hashing based on the Recovery of Subspace Structures" (RSSH) for image retrieval. Specifically, we firstly adapt the Low-rank Representation (LRR) model into a new variant which treats the real-world data as samples drawn from a union of several low-rank subspaces. Then, the pairwise similarities are represented in a space-and-time saving manner based on the learned low-rank correlation matrix of the modified LRR. Next, the challenging discrete graph hashing is employed for binary hashing codes. Notably, we convert the original graph hashing model into an optimization-friendly formalization, which is addressed with efficient closed-form solutions for its subproblems. Finally, the devised linear hash functions are fast achieved for out-of-samples. Retrieval experiments on four image datasets testify the superiority of RSSH to several state-of-the-art hashing models. Besides, it's worth mentioning that RSSH, a shallow model, significantly outperforms two recently proposed unsupervised deep hashing methods, which further confirms its effectiveness.

    Metadata

    Item Type: Article
    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: 02 Mar 2020 13:56
    Last Modified: 09 Aug 2023 12:47
    URI: https://eprints.bbk.ac.uk/id/eprint/30968

    Statistics

    Activity Overview
    6 month trend
    0Downloads
    6 month trend
    0Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item