@string{ivc="Image and Vision Computing"}
@string{elsevier="Elsevier"}
@Article{Kato2007,
  author =	 {Kato, Zoltan},
  title =	 {Segmentation of Color Images via Reversible Jump
                  {MCMC} Sampling},
  journal =	 ivc,
  year =	 2008,
  volume =	 26,
  number =	 3,
  pages =	 {361--371},
  month =	 mar,
  publisher =	 elsevier,
  pdf =		 {papers/ivc2005.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. The algorithm
                  has been validated on a database of real images with
                  human segmented ground truth.}
}