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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Stephen A. Burns
  • Vol. 26, Iss. 11 — Nov. 1, 2009
  • pp: 2434–2443

Fast color quantization using weighted sort-means clustering

M. Emre Celebi  »View Author Affiliations


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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