Robust Color Classification Using Fuzzy Rule-Based Particle Swarm Optimization

In this paper we present a novel approach for color classification in which an evolutionary algorithm optimizes a fuzzy system with least number of rules and minimum error rate by meaning of Particle Swarm Optimization (PSO) method. The aim of this work is to retrieve images according to their dominant(s) color(s) expressed through linguistic expressions, and implementation through a vision system. Fuzzy sets are defined on the H, S and L components of the HSL Color Space to provide a fuzzy logic model which aims to follow the human intuition of Color Classification. The Final system designed by this method is adaptive to continuous variable lighting according to its evolving-fuzzy nature, which is one of the challenging applications in this field.