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Model Fitting and Model Evidence for Multiscale Image Texture Analysis

M. Datcu, D. A. Stoichescu, K. Seidel and C. Iorga
American Institute of Physics, AIP Conference Proceedings 735
Vol. 735, pp. 35-42, November 2004

Abstract

This paper gives an overview of the two levels of Bayesian inference: model fitting and model selection and shows how they can be used for the image texture analysis. The applied models are the Gauss-Markov and Gibbs auto-binomial Random Fields. In the second part the article introduces a linear model for the image wavelet coefficients able to explain the full description of the spatial, inter-scale and inter-band behavior of a multi-resolution decomposed image. The model parameters, model variance and evidence are used to characterize the image texture.


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@Article{eth_biwi_00351,
  author = {M. Datcu and D. A. Stoichescu and K. Seidel and C. Iorga},
  title = {Model Fitting and Model Evidence for Multiscale Image Texture Analysis},
  journal = {American Institute of Physics, AIP Conference Proceedings 735},
  year = {2004},
  month = {November},
  pages = {35-42},
  volume = {735},
  number = {},
  keywords = {texture analysis, Bayesian inference, maximum entropy, model fitting}
}