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  • Cross-modal hashing with semantic deep embedding

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    Zhou208354.pdf (2.546Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Yan, Cheng
    Bai, Xiao
    Wang, Shuai
    Zhou, Jun
    Hancock, Edwin R
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2019
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    Abstract
    Cross-modal hashing has demonstrated advantages on fast retrieval tasks. It improves the quality of hash coding by exploiting semantic correlation across different modalities. In supervised cross-modal hashing, the learning of hash function replies on the quality of extracted features, for which deep learning models have been adopted to replace the traditional models based on handcraft features. All deep methods, however, have not sufficiently explored semantic correlation of modalities for the hashing process. In this paper, we introduce a novel end-to-end deep cross-modal hashing framework which integrates feature and ...
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    Cross-modal hashing has demonstrated advantages on fast retrieval tasks. It improves the quality of hash coding by exploiting semantic correlation across different modalities. In supervised cross-modal hashing, the learning of hash function replies on the quality of extracted features, for which deep learning models have been adopted to replace the traditional models based on handcraft features. All deep methods, however, have not sufficiently explored semantic correlation of modalities for the hashing process. In this paper, we introduce a novel end-to-end deep cross-modal hashing framework which integrates feature and hash-code learning into the same network. We take both between and within modalities data correlation into consideration, and propose a novel network structure and a loss function with dual semantic supervision for hash learning. This method ensures that the generated binary codes keep the semantic relationship of the original data points. Cross-modal retrieval experiments on commonly used benchmark datasets show that our method yields substantial performance improvement over several state-of-the-art hashing methods.
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    Journal Title
    NEUROCOMPUTING
    Volume
    337
    DOI
    https://doi.org/10.1016/j.neucom.2019.01.040
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Engineering
    Psychology
    Publication URI
    http://hdl.handle.net/10072/385460
    Collection
    • Journal articles

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