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• W2013109184 abstract "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." @default.
• W2013109184 created "2016-06-24" @default.
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• W2013109184 date "2014-01-09" @default.
• W2013109184 modified "2023-10-06" @default.
• W2013109184 title "Colour quantisation using the adaptive distributing units algorithm" @default.
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• W2013109184 doi "https://doi.org/10.1179/1743131x13y.0000000059" @default.
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