BMVC 2004, Kingston, 7th-9th Sept, 2004
Reversible Jump Markov Chain Monte Carlo for Unsupervised MRF Color Image Segmentation
Z. Kato (University of Szeged, Hungary)
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.
(pdf article)