Color Image Segmentation and Parameter Estimation in a Markovian Framework

Kato, Zoltan; Pong, Ting Chuen; Lee, John Chung Mong

by Zoltan Kato, Ting Chuen Pong, John Chung Mong Lee

Abstract:

An unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes.

Reference:

Zoltan Kato, Ting Chuen Pong, John Chung Mong Lee, Color Image Segmentation and Parameter Estimation in a Markovian Framework, In Pattern Recognition Letters, volume 22, no. 3-4, pp. 309-321, 2001.

Bibtex Entry:

@string{pattreclet="Pattern Recognition Letters"}
@Article{Kato-etal2001,
  author =	 {Kato, Zoltan and Pong, Ting Chuen and Lee, John
                  Chung Mong},
  title =	 {Color Image Segmentation and Parameter Estimation in
                  a {M}arkovian Framework},
  journal =	 pattreclet,
  year =	 2001,
  volume =	 22,
  number =	 {3-4},
  pages =	 {309--321},
  month =	 mar,
  keywords =	 {Unsupervised image segmentation, Color, Markov
                  random fields, Pixel classification, Parameter
                  estimation},
  pdf =		 {papers/pattreclet2001.pdf},
  abstract =	 {An unsupervised color image segmentation algorithm
                  is presented, using a Markov random field (MRF)
                  pixel classification model. We propose a new method
                  to estimate initial mean vectors effectively even if
                  the histogram does not have clearly distinguishable
                  peaks. The only parameter supplied by the user is
                  the number of classes.}
}