Nature-inspired framework for measuring visual image resemblance: A near rough set approach

  • Authors:

  • Sheela Ramanna;Amir H. Meghdadi;James F. Peters

  • Affiliations:

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

  • Venue:
  • Theoretical Computer Science
  • Year:
  • 2011

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Abstract

The problem considered in this paper is how to determine the degree of nearness between complex visual objects. The proposed solution to this problem stems from a natural computing approach to solving the visual acuity problem in terms of a granular representation of visual information that is quantifiable as well as understandable for humans. This is accomplished via a near rough set framework in the approximation of a pair of disjoint sets and measurement of distances between sets using various fuzzy pseudometrics. Pseudometrics, in general, and fuzzy pseudometrics, in particular, are useful in measuring the distance between pairs of objects such as sets. Such distances are indicators of the nearness of (resemblance between) visual objects. These observations lead to a number of practical applications such as object recognition and object retrieval in digital image analysis. One such application is reported in this article. The contribution of this article is threefold: introduction of a nature-inspired framework for measurement of visual object resemblance, four different incarnations of the standard fuzzy metric and application of fuzzy metrics in content-based image retrieval experiments.