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  • Maximum margin hashing with supervised information

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    Author(s)
    Yang, Haichuan
    Bai, Xiao
    Liu, Yanzhen
    Wang, Yanyang
    Bai, Lu
    Zhou, Jun
    Tang, Wenzhong
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2016
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    Abstract
    Binary code is a kind of special representation of data. With the binary format, hashing framework can be built and a large amount of data can be indexed to achieve fast research and retrieval. Many supervised hashing approaches learn hash functions from data with supervised information to retrieve semantically similar samples. This kind of supervised information can be generated from external data other than pixels. Conventional supervised hashing methods assume a fixed relationship between the Hamming distance and the similar (dissimilar) labels. This assumption leads to too rigid requirement in learning and makes the ...
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    Binary code is a kind of special representation of data. With the binary format, hashing framework can be built and a large amount of data can be indexed to achieve fast research and retrieval. Many supervised hashing approaches learn hash functions from data with supervised information to retrieve semantically similar samples. This kind of supervised information can be generated from external data other than pixels. Conventional supervised hashing methods assume a fixed relationship between the Hamming distance and the similar (dissimilar) labels. This assumption leads to too rigid requirement in learning and makes the similar and dissimilar pairs not distinguishable. In this paper, we adopt a large margin principle and define a Hamming margin to formulate such relationship. At the same time, inspired by support vector machine which achieves strong generalization capability by maximizing the margin of its decision surface, we propose a binary hash function in the same manner. A loss function is constructed corresponding to these two kinds of margins and is minimized by a block coordinate descent method. The experiments show that our method can achieve better performance than the state-of-the-art hashing methods.
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    Journal Title
    Multimedia Tools and Applications
    Volume
    75
    Issue
    7
    DOI
    https://doi.org/10.1007/s11042-015-3159-3
    Copyright Statement
    © 2016 Springer Netherlands. This is an electronic version of an article published in Multimedia Tools and Applications, Volume 75, Issue 7, pp 3955–3971, 2016. Multimedia Tools and Applications is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Information Systems not elsewhere classified
    Computer Software
    Distributed Computing
    Information Systems
    Artificial Intelligence and Image Processing
    Publication URI
    http://hdl.handle.net/10072/99679
    Collection
    • Journal articles

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