M. Hamidi - MSc. Student at Department of Electrical and Computer Engineering, Islamic Azad University of Qazvin
A. Borji - now working toward his PhD at the School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics

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. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions. 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. 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% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise.