Smooth Image Segmentation by Nonparametric Bayesian Inference

  • Peter Orbanz
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3951)


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 efficiently as clustering procedures in the strictly conjugate case. The sampling algorithm can process both single-channel and multi-channel image data. Experimental results are presented for real-world synthetic aperture radar and magnetic resonance imaging data.


Segmentation Result Base Measure Synthetic Aperture Radar Image Sampling Algorithm Dirichlet Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Orbanz
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.Institute of Computational ScienceETH ZurichSwitzerland

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