Genetic algorithm clustering for color image quantization

Clustering is an unsupervised classification method used for different issues in image analysis. Genetic algorithms are randomized search and optimisation techniques. In this paper, we present a genetic algorithm clustering for color image quantization as a prior process to any other one for image analysis. A fitness function with a smallest number of variables is proposed. It´s based on the fuzzy c-means objective function reformulated by Bezdek and the one proposed by Frigui and Krishnapuram in their competitive agglomeration algorithm. The proposed clustering genetic algorithm allows the initial population solutions to converge to good results in relatively less run-time. In addition, variable chromosome length is used to determine the clusters number.