Tolerance Classes in Measuring Image Resemblance

  • Authors:

  • A. H. Meghdadi;J. F. Peters;S. Ramanna

  • Affiliations:

  • Computational Intelligence Laboratory, Dept. Electrical & Computer Engineering, University of Manitoba, Winnipeg, Canada R3T 5V6;Computational Intelligence Laboratory, Dept. Electrical & Computer Engineering, University of Manitoba, Winnipeg, Canada R3T 5V6;Computational Intelligence Laboratory, Dept. Electrical & Computer Engineering, University of Manitoba, Winnipeg, Canada R3T 5V6 and Dept. Applied Computer Science, University of Winnipeg, Winnipe ...

  • Venue:
  • KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
  • Year:
  • 2009

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Abstract

The problem considered in this paper is how to measure resemblance between images. One approach to the solution to this problem is to find parts of images that resemble each other with a tolerable level of error. This leads to a consideration of tolerance relations that define coverings of images and measurement of the degree of overlap between tolerances classes in pairs of images. This approach is based on a tolerance class form of near sets that model human perception in a physical continuum. This is a humanistic perception-based near set approach, where tolerances become part of the solution to the image correspondence problem. Near sets are a generalization of rough sets introduced by Zdzisław Pawlak during the early 1980s. The basic idea in devising near set-based measures of resemblance of images that emulate human perception is to allow overlapping classes in image coverings defined with respect to a tolerance *** . The contribution of this article is the introduction of two new tolerance class-based image resemblance measures and a comparison of the new measures with the original Henry-Peters image nearness measure.