Image Modeling Using Gibbs-Markov Random Field and Support Vector Machines Algorithm

This paper introduces a novel approach to estimate the clique potentials of Gibbs Markov random field (GMRF) models using the Support Vector Machines (SVM) algorithm and the Mean Field (MF) theory. The proposed approach is based on modeling the potential function associated with each clique shape of the GMRF model as a Gaussian-shaped kernel. In turn, the energy function of the GMRF will be in the form of a weighted sum of Gaussian kernels. This formulation of the GMRF model urges the use of the SVM with the Mean Field theory applied for its learning for estimating the energy function. The approach has been tested on synthetic texture images and is shown to provide satisfactory results in retrieving the synthesizing parameters.

Authors:

• Refaat M Mohamed
• Ayman El-Baz
• Aly A. Farag

Keywords:

• Image Modeling
• MRF
• Parameters Estimation
• SVM Learning.

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