Title

Fuzzy Art For Relatively Fast Unsupervised Image Color Quantization

Keywords

Clustering; Fuzzy ART; Image color quantization; Unsupervised

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. © 2008 Springer Berlin Heidelberg.

Publication Date

12-1-2008

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

5342 LNCS

Number of Pages

147-156

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-540-89689-0_19

Socpus ID

58349114077 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/58349114077

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