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Nonparametric Bayesian image segmentation
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DOI：10.1007/s11263-007-0061-0.png)
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Abstract
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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.
Keywords:
Markov random fields
nonparametric Bayesian methods
Dirichlet process mixtures
image segmentation
clustering
spatial statistics
Markov chain
Monte Carlo
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