Image classification using probability higher-order local auto-correlations

  • Authors:

  • Tetsu Matsukawa;Takio Kurita

  • Affiliations:

  • University of Tsukuba, Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

  • Venue:
  • ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
  • Year:
  • 2009

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Abstract

In this paper, we propose a novel method for generic object recognition by using higher-order local auto-correlations on probability images The proposed method is an extension of bag-of-features approach to posterior probability images Standard bag-of-features is approximately thought as sum of posterior probabilities on probability images, and spatial co-occurrences of posterior probability are not utilized Thus, its descriptive ability is limited However, using local auto-correlations of probability images, the proposed method extracts richer information than the standard bag-of-features Experimental results show the proposed method is enable to have higher classification performances than the standard bag-of-features.