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    Flexible Cross-Modal Hashing

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    Name:
    TNNLS-2019-P-11759.pdf
    Size:
    1.762Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Yu, Guoxian cc
    Liu, Xuanwu
    Wang, Jun
    Domeniconi, Carlotta
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-10-14
    Online Publication Date
    2020-10-14
    Print Publication Date
    2020
    Submitted Date
    2019-07-11
    Permanent link to this record
    http://hdl.handle.net/10754/665599
    
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    Abstract
    Hashing has been widely adopted for large-scale data retrieval in many domains due to its low storage cost and high retrieval speed. Existing cross-modal hashing methods optimistically assume that the correspondence between training samples across modalities is readily available. This assumption is unrealistic in practical applications. In addition, existing methods generally require the same number of samples across different modalities, which restricts their flexibility. We propose a flexible cross-modal hashing approach (FlexCMH) to learn effective hashing codes from weakly paired data, whose correspondence across modalities is partially (or even totally) unknown. FlexCMH first introduces a clustering-based matching strategy to explore the structure of each cluster and, thus, to find the potential correspondence between clusters (and samples therein) across modalities. To reduce the impact of an incomplete correspondence, it jointly optimizes the potential correspondence, the crossmodal hashing functions derived from the correspondence, and a hashing quantitative loss in a unified objective function. An alternative optimization technique is also proposed to coordinate the correspondence and hash functions and reinforce the reciprocal effects of the two objectives. Experiments on public multimodal data sets show that FlexCMH achieves significantly better results than state-of-the-art methods, and it, indeed, offers a high degree of flexibility for practical cross-modal hashing tasks.
    Citation
    Yu, G., Liu, X., Wang, J., Domeniconi, C., & Zhang, X. (2020). Flexible Cross-Modal Hashing. IEEE Transactions on Neural Networks and Learning Systems, 1–11. doi:10.1109/tnnls.2020.3027729
    Sponsors
    This work was supported in part by the Natural Science Foundation of China under Grant 61872300, Grant 62031003, and Grant 62072380; and in part by the Qilu Scholar Startup Fund of Shandong University.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Neural Networks and Learning Systems
    DOI
    10.1109/tnnls.2020.3027729
    Additional Links
    https://ieeexplore.ieee.org/document/9223723/
    ae974a485f413a2113503eed53cd6c53
    10.1109/tnnls.2020.3027729
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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