title: Unsupervised clustering approaches to color classification for color-based image code recognition.
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.
authors.affiliation: ColorZip Media Inc., 9th Floor, KBL Center, 2 Nonhyeon-Dong, Gangnam-Gu, Seoul 135-811, South Korea.
authors.forename: Cheolho
authors.initials: C
authors.lastname: Cheong
authors.forename: Gordon
authors.initials: G
authors.lastname: Bowman
authors.forename: Tack-Don
authors.initials: TD
authors.lastname: Han