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

Jihong Fan, Ru-Ze Liang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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
Original languageEnglish (US)
Pages (from-to)733-743
Number of pages11
JournalNeural Computing and Applications
Issue number10
StatePublished - Sep 17 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The work was funded by Science and Technology project under Grant No. 12531826 of Education Department, Heilongjiang, China.


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