Parametric stochastic modeling for color image segmentation and texture characterization

Imtnan Ul Haque Qazi, Olivier Alata, Zoltan Kato

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationAdvanced Color Image Processing and Analysis
PublisherSpringer New York
Pages279-325
Number of pages47
Volume9781441961907
ISBN (Electronic)9781441961907
ISBN (Print)1441961895, 9781441961891
DOIs
Publication statusPublished - Nov 1 2013

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

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  • Cite this

    Qazi, I. U. H., Alata, O., & Kato, Z. (2013). Parametric stochastic modeling for color image segmentation and texture characterization. In Advanced Color Image Processing and Analysis (Vol. 9781441961907, pp. 279-325). Springer New York. https://doi.org/10.1007/978-1-4419-6190-7_9