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Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 47, Iss. 13 — May. 1, 2008
  • pp: 2326–2345

Unsupervised clustering approaches to color classification for color-based image code recognition

Cheolho Cheong, Gordon Bowman, and Tack-Don Han  »View Author Affiliations

Applied Optics, Vol. 47, Issue 13, pp. 2326-2345 (2008)

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Color-vision-based applications for mobile phones has become a subject of special interest lately. It would be interesting to investigate an unsupervised, adaptive, and fast algorithm that can classify color components into color clusters. We propose a hierarchical clustering approach using a single-linkage algorithm and a k-means clustering approach to color classification for color-based image code recognition in mobile computing environments. We also measured the performance of the proposed algorithms by color channel stretch, which is a simple color-correction method. Experimental results show that the single-linkage method is more robust than previous algorithms used in experiments with varying cameras and print materials. In particular the k-means-based method with color channel stretching has the highest performance and is the most robust under varying environment conditions such as illuminants, cameras, and print materials.

© 2008 Optical Society of America

OCIS Codes
(330.0330) Vision, color, and visual optics : Vision, color, and visual optics
(330.1720) Vision, color, and visual optics : Color vision

ToC Category:
Vision, color, and visual optics

Original Manuscript: November 5, 2007
Revised Manuscript: January 15, 2008
Manuscript Accepted: February 16, 2008
Published: April 28, 2008

Virtual Issues
Vol. 3, Iss. 6 Virtual Journal for Biomedical Optics

Cheolho Cheong, Gordon Bowman, and Tack-Don Han, "Unsupervised clustering approaches to color classification for color-based image code recognition," Appl. Opt. 47, 2326-2345 (2008)

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