Nonparametric Bayesian Image Segmentation |
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Authors: | Peter Orbanz Joachim M Buhmann |
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Affiliation: | (1) Institute of Computational Science, ETH Zürich, Universitaet-Strasse 6, ETH Zentrum, CAB G 84.1, Zurich, 8092, Switzerland |
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Abstract: | 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 real-world gray value images, synthetic aperture radar images and magnetic resonance imaging data. |
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Keywords: | Markov random fields Nonparametric Bayesian methods Dirichlet process mixtures Image segmentation Clustering Spatial statistics Markov chain Monte Carlo |
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