Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
527130 | Image and Vision Computing | 2011 | 12 Pages |
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.
Research Highlights► k-means clustering algorithm is investigated from a color quantization perspective. ► Fast and exact k-means variants that utilize data reduction, sample weighting, and accelerated nearest neighbor search are introduced. ► Presented k-means implementations outperformed state-of-the-art quantization methods on classic test images. ► Other advantages of the presented methods include ease of implementation, high computational speed, and the possibility of incorporating spatial information.