An Improved Fuzzy Algorithm for Image Segmentation

In this paper we propose a color classification algorithm in which Particle Swarm Optimization method optimizes a fuzzy system for Color Classification and Image Segmentation with least number of rules and minimum error rate. In this approach each particle of the swarm codes a set of fuzzy rules. During evolution, each member of population tries to maximize a fitness norm which has designed due to high classification rate and small number of rules. Finally, the particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Fuzzy sets are defined on the H, S and L components of the HSL Color Space to provide a fuzzy-based model which aims to follow the human intuition of Color Classification. Color-based vision applications face the challenge of color variations by illumination. The Final system designed by this method is adaptive to continuous variable lighting according to its evolving-fuzzy nature. In this method parameters setting's done only once. The experimental results in RoboCup leagues demonstrate that the presented approach can be very robust to noise and light variations.