Colour quantisation using the adaptive distributing units algorithm

Colour quantisation (CQ) is an important operation with many applications in graphics and image processing. Most CQ methods are essentially based on data clustering algorithms one of which is the popular k-means algorithm. Unfortunately, like many batch clustering algorithms, k-means is highly sensi...

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Bibliographic Details
Published in:The imaging science journal Vol. 62; no. 2; pp. 80 - 91
Main Authors: Celebi, M. E., Hwang, S., Wen, Q.
Format: Journal Article
Language:English
Published: Taylor & Francis 01.02.2014
Subjects:
Clustering
Colour quantization
Competitive learning
K-means
ISSN:1368-2199, 1743-131X
Online Access:Get full text
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Summary:Colour quantisation (CQ) is an important operation with many applications in graphics and image processing. Most CQ methods are essentially based on data clustering algorithms one of which is the popular k-means algorithm. Unfortunately, like many batch clustering algorithms, k-means is highly sensitive to the selection of the initial cluster centres. In this paper, we adapt Uchiyama and Arbib's competitive learning algorithm to the CQ problem. In contrast to the batch k-means algorithm, this online clustering algorithm does not require cluster centre initialisation. Experiments on a diverse set of publicly available images demonstrate that the presented method outperforms some of the most popular quantisers in the literature.
ISSN:1368-2199
1743-131X
DOI:10.1179/1743131X13Y.0000000059