Issue No. 12 - December (1994 vol. 16)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.387488
<p>Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results.</p>
image colour analysis; image segmentation; unsupervised learning; least squares approximations; convergence of numerical methods; vector quantisation; color image segmentation; competitive learning; color space clustering; least sum-of-squares criterion; convergence; optimum solution approximation; color scenes; efficiency; color coordinates
M. Arbib and T. Uchiyama, "Color Image Segmentation using Competitive Learning," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 1197-1206, 1994.