Reference : A spatially constrained generative model and an EM algorithm for image segmentation
Scientific journals : Article
Engineering, computing & technology : Computer science
A spatially constrained generative model and an EM algorithm for image segmentation
Diplaros, Aristeidis [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Gevers, Theo [> >]
IEEE Transactions on Neural Networks
Yes (verified by ORBilu)
[en] bound optimization ; expectation-maximization (EM) algorithm ; hidden Markov random fields (MRFs) ; image segmentation ; spatial clustering
[en] In this paper, we present a novel spatially constrained generative model and an expectation-maximization (EM) algorithm for model-based image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions with similar parameters, where similarity is defined by entropic quantities relating to the neighboring priors. In order to estimate model pa rameters from observations, we derive a spatially constrained EM algorithm that iteratively maximizes a lower bound on the data log-likelihood, where the penalty term is data-dependent. Our algorithm is very easy to implement and is similar to the standard EM algorithm for Gaussian mixtures with the main difference that the labels posteriors are "smoothed" over pixels between each E-and M-step by a standard image filter. Experiments on synthetic and real images show that our algorithm achieves competitive segmentation results compared to other Markov-based methods, and is in general faster.

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