Batch neural gas with deterministic initialization for color quantization

Authors:
M. Emre Celebi;Quan Wen;Gerald Schaefer;Huiyu Zhou
Affiliations:
Department of Computer Science, Louisiana State University, Shreveport, LA;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Department of Computer Science, Loughborough University, Loughborough, UK;The Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK
Venue:
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Year:
2012

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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.