@InProceedings{Kato-etal97,
  author =	 {Kato, Zoltan and Pong, Ting Chuen and Lee, John
                  Chung Mong},
  title =	 {Color Image Classification and Parameter Estimation
                  in a {M}arkovian Framework},
  booktitle =	 {Proceedings of Workshop on 3D Computer Vision},
  pages =	 {75--79},
  year =	 1997,
  editor =	 {Tsui, Hung Tat and Chung, Chi Kit Ronald},
  address =	 {The Chinese University of Hong Kong, Hong Kong},
  month =	 may,
  pdf =		 {papers/3dcv97.pdf},
  ps =		 {papers/3dcv97.ps},
  abstract =	 {In this paper, we propose an unsupervised color
                  image classification algorithm based on a Markov
                  random field (MRF) model. In the MRF model, we use
                  the CIE-luv color metric because it is close
                  to human perception when computing color
                  differences. On the other hand, intensity and chroma
                  information is separated in this space. Without
                  parameter estimation, our model would not be useful
                  in real-life applications. We propose herein a new
                  method to estimate mean vectors effectively even if
                  the observed image is very noisy and the histogram
                  does not have clearly distinguishable peaks. These
                  values are then used in a more complex, iterative
                  estimation process as initial values. The only
                  parameter supplied by the user is the number of
                  classes. All other parameters are estimated from the
                  observed image. The algorithm has been tested on a
                  variety of real images (indoor, outdoor), noisy
                  video sequences and noisy synthetic images.}
}