Acceleration of K-Means and Related Clustering Algorithms
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Fast Recognition of Musical Genres Using RBF Networks
IEEE Transactions on Knowledge and Data Engineering
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Reduct and variance based clustering of high dimensional dataset
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
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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 kmeans with different initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a set of classic test images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.