Bayesian joint estimation of the multifractality parameter of image patches using gamma Markov Random Field priors

Sebastien Combrexelle, Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Texture analysis can be embedded in the mathematical framework of multifractal (MF) analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, wavelet leaders. A statistical model for leaders was proposed permitting Bayesian estimation of MF parameters for images yielding improved estimation quality over linear regression based estimation. This present work proposes an extension of this Bayesian model for patch-wise MF analysis of images. Classical MF analysis assumes space homogeneity of the MF properties whereas here we assume MF properties may change between texture elements and we do not know where the changes are located. This paper proposes a joint Bayesian model for patches formulated using spatially smoothing gamma Markov Random Field priors to counterbalance the increased statistical variability of estimates caused by small patch sizes. Numerical simulations based on synthetic multi-fractal images demonstrate that the proposed algorithm outperforms previous formulations and standard estimators.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages4468-4472
Number of pages5
ISBN (Electronic)9781467399616
DOIs
Publication statusPublished - 19 Aug 2016
Event23rd IEEE International Conference on Image Processing - Phoenix Convention Center, Phoenix, United States
Duration: 25 Sep 201628 Sep 2016

Publication series

NameIEEE International Conference on Image Processing
PublisherIEEE
ISSN (Print)2381-8549

Conference

Conference23rd IEEE International Conference on Image Processing
Abbreviated titleICIP 2016
CountryUnited States
CityPhoenix
Period25/09/1628/09/16

Keywords

  • Bayesian Estimation
  • Gamma Markov Random Field
  • Multifractal Analysis
  • Texture Analysis
  • Wavelet Leaders

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Combrexelle, S., Wendt, H., Altmann, Y., Tourneret, J-Y., McLaughlin, S., & Abry, P. (2016). Bayesian joint estimation of the multifractality parameter of image patches using gamma Markov Random Field priors. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 4468-4472). (IEEE International Conference on Image Processing). IEEE. https://doi.org/10.1109/ICIP.2016.7533205