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

• Type: Journal
• Year: 2008
• Where: IJCV
• Authors: Peter Orbanz, Joachim M. Buhmann
• Keywords: Dirichlet Process, IJCV 2006, IJCV 2008, Image Segmentation, Nonparametric Bayesian