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Paper   IPM / Cognitive Sciences / 9587
School of Cognitive Sciences
  Title:   CLPSO-based Fuzzy Color Image Segmentation
  Author(s): 
1.  Ali Borji
2.  Mandana Hamidi
3.  A.M. Eftekhari Moghadam
  Status:   In Proceedings
  Proceeding: Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
  Year:  2007
  Supported by:  IPM
  Abstract:
A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89

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