Kohonen Networks and Clustering: Comparative Performance in Color Clustering
Abstract
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.
Cite
Text
Snyder et al. "Kohonen Networks and Clustering: Comparative Performance in Color Clustering." Neural Information Processing Systems, 1990.Markdown
[Snyder et al. "Kohonen Networks and Clustering: Comparative Performance in Color Clustering." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/snyder1990neurips-kohonen/)BibTeX
@inproceedings{snyder1990neurips-kohonen,
title = {{Kohonen Networks and Clustering: Comparative Performance in Color Clustering}},
author = {Snyder, Wesley and Nissman, Daniel and Van den Bout, David and Bilbro, Griff},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {984-990},
url = {https://mlanthology.org/neurips/1990/snyder1990neurips-kohonen/}
}