Parametric Stochastic Modeling for Color Image Segmentation and Texture Characterization

Chapter

Abstract

Black should be made a color of light Clemence Boulouque

Parametric stochastic models offer the definition of color and/or texture features based on model parameters, which is of interest for color texture classification, segmentation and synthesis.

In this chapter, distribution of colors in the images through various parametric approximations including multivariate Gaussian distribution, multivariate Gaussian mixture models (MGMM) and Wishart distribution, is discussed. In the context of Bayesian color image segmentation, various aspects of sampling from the posterior distributions to estimate the color distribution from MGMM and the label field, using different move types are also discussed. These include reversible jump mechanism from MCMC methodology. Experimental results on color images are presented and discussed.

Then, we give some materials for the description of color spatial structure using Markov Random Fields (MRF), and more particularly multichannel GMRF, and multichannel linear prediction models. In this last approach, two dimensional complex multichannel versions of both causal and non-causal models are discussed to perform the simultaneous parametric power spectrum estimation of the luminance and the chrominance channels of the color image. Application of these models to the classification and segmentation of color texture images is also illustrated.

Keywords

Stochastic models Multivariate Gaussian mixture models Wishart distribution Multichannel complex linear prediction models Gaussian Markov Random Field Parametric spectrum estimation Color image segmentation Color texture classification Color texture segmentation Reversible jump Markov chain Monte Carlo 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Imtnan-Ul-Haque Qazi
    • 1
  • Olivier Alata
    • 2
  • Zoltan Kato
    • 3
  1. 1.SPARCENT IslamabadPakistan Space & Upper Atmosphere Research CommissionIslamabadPakistan
  2. 2.Laboratory Hubert Curien, UMR CNRS 5516Jean Monnet UniversitySaint-EtienneFrance
  3. 3.Department of Image Processing and Computer Graphics, Institute of InformaticsUniversity of SzegedSzegedHungary

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