Unsupervised clustering approaches to color classification for color-based image code recognition
Applied Optics, Vol. 47, Issue 13, pp. 2326-2345 (2008)
http://dx.doi.org/10.1364/AO.47.002326
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Abstract
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
History
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
Citation
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)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-47-13-2326
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