Nonparametric Bayesian Image Segmentation

12 years 5 months ago
Nonparametric Bayesian Image Segmentation
Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogram clustering which automatically determines the number of segments when spatial smoothness constraints on the class assignments are enforced by a Markov Random Field. A Dirichlet process prior controls the level of resolution which corresponds to the number of clusters in data with a unique cluster structure. The resulting posterior is efficiently sampled by a variant of a conjugate-case sampling algorithm for Dirichlet process mixture models. Experimental results are provided for realworld gray value images, synthetic aperture radar images and magnetic resonance imaging data. Keywords Markov random fields
Peter Orbanz, Joachim M. Buhmann
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2008
Where IJCV
Authors Peter Orbanz, Joachim M. Buhmann
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