Name
Abstract | ||
|---|---|---|
A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints. This model exploits the Dirichlet compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss-Markov random field (MRF) on the Dirichlet parameters to impose smoothness. The main advantages of this model are two. First, it explicitly models the mixing proportions as probability vectors and simultaneously imposes spatial smoothness. Second, it results in closed form parameter updates using a maximum a posteriori (MAP) expectation-maximization (EM) algorithm. Previous efforts on this problem used models that did not model the mixing proportions explicitly as probability vectors or could not be solved exactly requiring either time consuming Markov Chain Monte Carlo (MCMC) or inexact variational approximation methods. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation compared to other GMM-based approaches. The model is also successfully compared to state of the art image segmentation methods in clustering both natural images and images degraded by noise. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TIP.2010.2047903 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
gaussian mixture model,probability density,new bayesian model,probability vector,spatially varying mixture,bayesian framework,image segmentation,art image segmentation method,spatial smoothness,spatial smoothness constraint,natural image,gaussian distribution,finite mixture model,monte carlo methods,markov chain monte carlo,degradation,maximum likelihood estimation,bayesian model,bayesian methods,rate distortion theory,gaussian processes,gaussian mixture models,clustering algorithms,expectation maximization,multinomial distribution,em algorithm,markov processes | Scale-space segmentation,Pattern recognition,Markov chain Monte Carlo,Markov model,Expectation–maximization algorithm,Image segmentation,Artificial intelligence,Dirichlet distribution,Maximum a posteriori estimation,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
19 | 9 | 1941-0042 |
Citations | PageRank | References |
38 | 1.10 | 50 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. Nikou | 1 | 679 | 46.56 |
| aristidis likas | 2 | 1926 | 140.40 |
| Nikolaos P Galatsanos | 3 | 145 | 5.39 |