%0 Journal Article
%T Improving the Performance of K-Means for Color Quantization
%A M. Emre Celebi
%J Computer Science
%D 2011
%I arXiv
%R 10.1016/j.imavis.2010.10.002
%X Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.
%U http://arxiv.org/abs/1101.0395v1