Intuitionistic Fuzzy Clustering with Applications in Computer Vision

  • Authors:

  • Dimitris K. Iakovidis;Nikos Pelekis;Evangelos Kotsifakos;Ioannis Kopanakis

  • Affiliations:

  • Dept. of Methodology, History & Theory of Science, University of Athens, Athens, Greece;Dept. of Informatics, University of Piraeus, Piraeus, Greece;Dept. of Informatics, University of Piraeus, Piraeus, Greece;Technological Educational Institute of Crete, Heraklion Crete, Greece

  • Venue:
  • ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
  • Year:
  • 2008

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Abstract

Intuitionistic fuzzy sets are generalized fuzzy sets whose elements are characterized by a membership, as well as a non-membership value. The membership value indicates the degree of belongingness, whereas the non-membership value indicates the degree of non-belongingness of an element to that set. The utility of intuitionistic fuzzy sets theory in computer vision is increasingly becoming apparent, especially as a means to coping with noise. In this paper, we investigate the issue of clustering intuitionistic fuzzy image representations. To achieve that we propose a clustering approach based on the fuzzy c-means algorithm utilizing a novel similarity metric defined over intuitionistic fuzzy sets. The performance of the proposed algorithm is evaluated for object clustering in the presence of noise and image segmentation. The results indicate that clustering intuitionistic fuzzy image representations can be more effective, noise tolerant and efficient as compared with the conventional fuzzy c-means clustering of both crisp and fuzzy image representations.