Fuzzy ART for Relatively Fast Unsupervised Image Color Quantization

  • Nicholas Shorter
  • Takis Kasparis
Conference paper

DOI: 10.1007/978-3-540-89689-0_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)
Cite this paper as:
Shorter N., Kasparis T. (2008) Fuzzy ART for Relatively Fast Unsupervised Image Color Quantization. In: da Vitoria Lobo N. et al. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2008. Lecture Notes in Computer Science, vol 5342. Springer, Berlin, Heidelberg

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.

Keywords

Image Color Quantization Fuzzy ART Clustering Unsupervised 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nicholas Shorter
    • 1
  • Takis Kasparis
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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