Smooth Image Segmentation by Nonparametric Bayesian Inference

@inproceedings{Orbanz2006SmoothIS,
  title={Smooth Image Segmentation by Nonparametric Bayesian Inference},
  author={Peter Orbanz and Joachim M. Buhmann},
  booktitle={ECCV},
  year={2006}
}
A nonparametric Bayesian model for histogram clustering is proposed to automatically determine the number of segments when Markov Random Field constraints enforce smooth class assignments. The nonparametric nature of this model is implemented by a Dirichlet process prior to control the number of clusters. The resulting posterior can be sampled by a modification of a conjugate-case sampling algorithm for Dirichlet process mixture models. This sampling procedure estimates segmentations as… CONTINUE READING
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