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. (C) 1997 Pattern Recognition Society.