@string{caip="Proceedings of International Conference on Computer Analysis of Images and Patterns"}
@string{lncs="Lecture Notes in Computer Science"}
@string{springer="Springer"}
@InProceedings{Kato-Pong2001,
  author =	 {Kato, Zoltan and Pong, Ting Chuen},
  title =	 {A {M}arkov Random Field Image Segmentation Model
                  Using Combined Color and Texture Features},
  booktitle =	 caip,
  pages =	 {547--554},
  year =	 2001,
  editor =	 {Skarbek, Wladyslaw},
  volume =	 2124,
  series =	 lncs,
  address =	 {Warsaw, Poland},
  month =	 sep,
  publisher =	 springer,
  pdf =		 {papers/caip2001.pdf},
  keywords =     {image segmentation, Markov random field model},
  abstract =	 {In this paper, we propose a Markov random field
                  (MRF) image segmentation model which aims at
                  combining color and texture features. The
                  theoretical framework relies on Bayesian estimation
                  associated with combinatorial optimization
                  (Simulated Annealing). The segmentation is obtained
                  by classifying the pixels into different pixel
                  classes. These classes are represented by
                  multi-variate Gaussian distributions. Thus, the only
                  hypothesis about the nature of the features is that
                  an additive white noise model is suitable to
                  describe the feature values belonging to a given
                  class. Herein, we use the perceptually uniform
                  CIE-L*u*v* color values as color features and a set
                  of Gabor filters as texture features. We provide
                  experimental results that illustrate the performance
                  of our method on both synthetic and natural color
                  images. Due to the local nature of our MRF model,
                  the algorithm can be highly parallelized.}
}