Paper Info
Title
Segmentation of color images via reversible jump MCMC sampling
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.
Year
DOI
Venue
2008
10.1016/j.imavis.2006.12.004
Image Vision Comput.
Keywords
Field
DocType
associated model parameter,markov random fields,simulated annealing,multivariate gaussian distribution,reversible jump markov chain monte carlo,color image,multivariate gaussian mixture identification,parameter estimation,image segmentation,mcmc sampling,unsupervised image segmentation,proposed algorithm,order markov,normal mixture identification,new rjmcmc sampler,color,order potential,reversible jump markov chain,reversible markov chain sampler,first order,ground truth,second order,gaussian distribution
Markov chain Monte Carlo,Pattern recognition,Markov random field,Segmentation,Markov chain,Reversible-jump Markov chain Monte Carlo,Multivariate normal distribution,Artificial intelligence,Maximum a posteriori estimation,Real image,Mathematics
Journal
Volume
Issue
ISSN
26
3
Image and Vision Computing
Citations 
PageRank 
References 
18
0.70
17
Authors
1
Name
Order
Citations
PageRank
Zoltan Kato126528.28