Fast Color Quantization Using Weighted Sort-Means Clustering

Details Fast Color Quantization Using Weighted Sort-Means Clustering Fast Color Quantization Using Weighted Sort-Means Clustering

2010-10-30

arxiv.org/abs/1011.0093v1

Journal of the Optical Society of America A 26 (2009) 2434-2443

Computer Science

Computer Vision and Pattern Recognition

30 pages, 2 figures, 4 tables

Scientific paper

10.1364/JOSAA.26.002434

Color quantization is an important operation with numerous 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, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.

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