Nonparametric Bayesian Image Segmentation
Peter Orbanz and Joachim Buhmann
Department of Computer Science, ETH Zurich, Switzerland.
Image segmentation algorithms partition the set of pixels of an
image into a specific number of different, spatially homogeneous groups. A nonparametric Bayesian model for histogram clustering is proposed 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
number of clusters. 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 real-world gray value images, synthetic aperture radar images and magnetic resonance imaging data.