@string{bmvc="Proceedings of British Machine Vision Conference"}
@InProceedings{Kato2004a,
  author =	 {Kato, Zoltan},
  title =	 {Reversible Jump {M}arkov Chain {M}onte {C}arlo for
                  Unsupervised {MRF} Color Image Segmentation},
  booktitle =	 bmvc,
  pages =	 {37--46},
  year =	 2004,
  editor =	 {Hoppe, Andreas and Barman, Sarah and Ellis, Tim},
  volume =	 {1},
  address =	 {Kingston, UK},
  month =	 sep,
  organization = {BMVA},
  ps =           {papers/bmvc2004.ps},
  pdf =		 {papers/bmvc2004.pdf},
  abstract =	 {Reversible jump Markov chain Monte Carlo (RJMCMC) is
                  a recent method which makes it possible to construct
                  reversible Markov chain samplers that jump between
                  parameter subspaces of different dimensionality. In
                  this paper, we propose a new RJMCMC sampler for
                  multivariate Gaussian mixture identification and we
                  apply it to color image segmentation. For this
                  purpose, we consider a first order Markov random
                  field (MRF) model where the singleton energies
                  derive from a multivariate Gaussian distribution and
                  second order potentials favor similar classes in
                  neighboring pixels. The proposed algorithm finds the
                  most likely number of classes, their associated
                  model parameters and generates a segmentation of the
                  image by classifying the pixels into these
                  classes. The estimation is done according to the
                  Maximum A Posteriori (MAP) criterion. Experimental
                  results are promising, we have obtained accurate
                  results on a variety of real color images.}
}