Parameter Estimation for Inhomogeneous Markov Random Fields Using PseudoLikelihood
نویسندگان
چکیده
We describe an algorithm for locally-adaptive parameter estimation of spatially inhomogeneous Markov random elds (MRFs). In particular, we establish that there is a unique solution which maximizes the local pseudo-likelihood in the inhomogeneous MRF model. Subsequently we demonstrate how Besag's iterative conditional mode (ICM) procedure can be generalized from homogeneous MRFs to inhomogeneous MRFs using this fact. This leads to an eecient local algorithm for parameter estimation in inhomoge-neous MRFs. Experimental results on synthetic images clearly illustrate the utility of the method, showing considerable improvement in segmentation accuracy resulting from the proposed approach compared to homogeneous MRFs or non-spatial segmentation using the k-means algorithm.
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