## Fast color quantization using weighted sort-means clustering

JOSA A, Vol. 26, Issue 11, pp. 2434-2443 (2009)

http://dx.doi.org/10.1364/JOSAA.26.002434

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### Abstract

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 the 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.

© 2009 Optical Society of America

**OCIS Codes**

(100.2000) Image processing : Digital image processing

(100.5010) Image processing : Pattern recognition

**ToC Category:**

Image Processing

**History**

Original Manuscript: April 22, 2009

Revised Manuscript: July 24, 2009

Manuscript Accepted: August 15, 2009

Published: October 27, 2009

**Citation**

M. Emre Celebi, "Fast color quantization using weighted sort-means clustering," J. Opt. Soc. Am. A **26**, 2434-2443 (2009)

http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-26-11-2434

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