A genetic c-Means clustering algorithm applied to color image quantization

This paper describes a novel data clustering algorithm, which is a hybrid approach combining a genetic algorithm with the classical c-means clustering algorithm (CMA). The proposed technique is superior to CMA in the sense that it converges to a nearby global optimum rather than a local one. As an application the problem of color image quantization is elaborated. Here, it is shown that substantial improvement of image quality is obtained by using the genetic approach.