Kohonen Networks and Clustering: Comparative Performance in Color Clustering

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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Wesley Snyder, Daniel Nissman, David Van den Bout, Griff Bilbro


The problem of color clustering is defined and shown to be a problem of assigning a large number (hundreds of thousands) of 3-vectors to a small number (256) of clusters. Finding those clusters in such a way that they best represent a full color image using only 256 distinct colors is a burdensome computational problem. In this paper, the problem is solved using "classical" techniques -- k-means clustering, vector quantization (which turns out to be the same thing in this application), competitive learning, and Kohonen self-organizing feature maps. Quality of the result is judged subjectively by how much the pseudo-color result resembles the true color image, by RMS quantization error, and by run time. The Kohonen map provides the best solution.