Improving the performance of k-means for color quantization |
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Authors: | M Emre Celebi |
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Affiliation: | Department of Computer Science, Louisiana State University, Shreveport, LA, USA |
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Abstract: | 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. |
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Keywords: | Color quantization Color reduction Clustering k-means |
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