期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 18, 期 3, 页码 798-808出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2007.891190
关键词
bound optimization; expectation-maximization (EM) algorithm; hidden Markov random fields (MRFs); image segmentation; spatial clustering
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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