Supervised MLE-Based Parameter Learning

  • Georgy L. Gimel’farb
Part of the Computational Imaging and Vision book series (CIVI, volume 16)


This chapter shows that the Gibbs models with multiple pairwise pixel interaction proposed in Chapter 2 have almost the same schemes of supervised parameter learning. The learning scheme was proposed first for homogeneous textures in (Gimel’farb, 1996a) and then generalized to piecewise‐homogeneous ones in (Gimel’farb, 1996b, 1996c). It recovers both the interaction structure and potentials from a given training sample (grayscale image or/and region map) by starting from an analytic first approximation of the MLE of the potentials. The approximation, computed from signal histograms that are the sufficient statistics of a particular Gibbs model, enables to compare relative strengths of a great many possible pairwise interactions and recover most characteristic clique families for represent­ing a given texture type. Then, the desired MLE of the potentials for the chosen characteristic clique families [C a : aA] is obtained by stochastic approximation similar to introduced by Younes (1988).


Training Sample Gray Level Stochastic Approximation Marginal Probability Marginal Probability Distribution 
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 Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Georgy L. Gimel’farb
    • 1
  1. 1.The University of AucklandAucklandNew Zealand

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