A comparison of clustering algorithms applied to color image quantization

Abstract

In this paper color image quantization by clustering is discussed. A clustering scheme, based on competitive learning is constructed and compared to the well-known C-means clustering algorithm. It is demonstrated that both perform equally well, but that the former is superior to the latter with respect to computing time. However, both depend on the initial conditions and may end up in local optima. Based on these findings, a hierarchical competitive learning scheme is constructed which is completely independent of initial conditions. The hierarchical approach is a hybrid structure between competitive learning and splitting of the color space. For comparison, a genetic approach is applied, which is a hybrid structure between a genetic algorithm and C-means clustering. The latter was demonstrated in the past to obtain global optimal results, but with high computational load. The hierarchical clustering scheme is shown to obtain near-global optimal results with low computational load.