Batch neural gas with deterministic initialization for color quantization

Abstract

Color quantization is an important operation with many applications in graphics and image processing. Clustering methods based on the competitive learning paradigm, in particular self-organizing maps, have been extensively applied to this problem. In this paper, we investigate the performance of the batch neural gas algorithm as a color quantizer. In contrast to self-organizing maps, this competitive learning algorithm does not impose a fixed topology and is insensitive to initialization. Experiments on publicly available test images demonstrate that, when initialized by a deterministic preclustering method, the batch neural gas algorithm outperforms some of the most popular quantizers in the literature. © 2012 Springer-Verlag Berlin Heidelberg.