Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/1258
Title: Fuzzy ART for Relatively Fast Unsupervised Image Color Quantization
Authors: Shorter, Nicholas S.
Kasparis, Takis 
Keywords: Image Color Quantization
Fuzzy ART
Clustering
Unsupervised
Issue Date: 2008
Publisher: Springer Berlin / Heidelberg
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5342 LNCS, pp. 147-156
Abstract: The use of Fuzzy Adaptive Resonance Theory (FA) is explored for the unsupervised color quantization of a color image. The red, green and blue color component values of a given color image are passed as input instances into FA which then groups similar colors into the same class. The average of all of the colors in a given class then replaces the pixel values whose original colors belonged to that class. The FA unsupervised clustering is capable of realizing color quantization with competitive accuracy and arguably low computation time.
URI: http://ktisis.cut.ac.cy/handle/10488/1258
ISBN: 9783540896883
ISSN: 0302-9743 (Print)
1611-3349 (Online)
DOI: 10.1007/978-3-540-89689-0_19
Rights: © Springer
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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