Learning a fine vocabulary

  • Authors:
  • Andrej Mikulík;Michal Perdoch;Ondřej Chum;Jiří Matas

  • Affiliations:
  • CMP, Dept. of Cybernetics, Faculty of EE, Czech Technical University in Prague;CMP, Dept. of Cybernetics, Faculty of EE, Czech Technical University in Prague;CMP, Dept. of Cybernetics, Faculty of EE, Czech Technical University in Prague;-

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
  • Year:
  • 2010

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Abstract

A novel similarity measure for bag-of-words type large scale image retrieval is presented. The similarity function is learned in an unsupervised manner, requires no extra space over the standard bag-of-words method and is more discriminative than both L2-based soft assignment and Hamming embedding. We show experimentally that the novel similarity function achieves mean average precision that is superior to any result published in the literature on a number of standard datasets. At the same time, retrieval with the proposed similarity function is faster than the reference method.