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A Variational Bayes Approach to Image Segmentation

  • Giuseppe Boccignone
  • Mario Ferraro
  • Paolo Napoletano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)

Abstract

In this note we will discuss how image segmentation can be handled by using Bayesian learning and inference. In particular variational techniques relying on free energy minimization will be introduced. It will be shown how to embed a spatial diffusion process on segmentation labels within the Variational Bayes learning procedure so to enforce spatial constraints among labels.

Keywords

Image Segmentation Gaussian Mixture Model Hide Variable Hide State Free Energy Minimization 
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 2007

Authors and Affiliations

  • Giuseppe Boccignone
    • 1
  • Mario Ferraro
    • 2
  • Paolo Napoletano
    • 1
  1. 1.Natural Computation Lab, DIIIE - Universitá di Salerno, via Ponte Don Melillo, 1 Fisciano (SA)Italy
  2. 2.DFS - Universitá di Torino, via Pietro Giuria 1, TorinoItaly

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