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    Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

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    Type
    Article
    Authors
    Fan, Jihong
    Liang, Ru-Ze cc
    KAUST Department
    Material Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2016-09-17
    Online Publication Date
    2016-09-17
    Print Publication Date
    2018-05
    Permanent link to this record
    http://hdl.handle.net/10754/622254
    
    Metadata
    Show full item record
    Abstract
    Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD-based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stochastic learning framework, we have one triplet of bags, including one basic bag, one positive bag, and one negative bag. These bags are mapped to histograms using a multi-instance dictionary. We argue that the EMD between the basic histogram and the positive histogram should be smaller than that between the basic histogram and the negative histogram. Base on this condition, we design a hinge loss. By minimizing this hinge loss and some regularization terms of the dictionary, we update the dictionary instances. The experiments over multi-instance retrieval applications shows its effectiveness when compared to other dictionary learning methods over the problems of medical image retrieval and natural language relation classification. © 2016 The Natural Computing Applications Forum
    Citation
    Fan J, Liang R-Z (2016) Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison. Neural Computing and Applications. Available: http://dx.doi.org/10.1007/s00521-016-2603-2.
    Sponsors
    The work was funded by Science and Technology project under Grant No. 12531826 of Education Department, Heilongjiang, China.
    Publisher
    Springer Nature
    Journal
    Neural Computing and Applications
    DOI
    10.1007/s00521-016-2603-2
    arXiv
    1609.00817
    Additional Links
    http://link.springer.com/article/10.1007%2Fs00521-016-2603-2
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00521-016-2603-2
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Material Science and Engineering Program

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