2 research outputs found
Improving the Performance of K-Means for Color Quantization
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.Comment: 26 pages, 4 figures, 13 table