Skip to Main content Skip to Navigation
Conference papers

Multiscale skewed heavy tailed model for texture analysis

Abstract : This paper deals with texture analysis based on multiscale stochastic modeling. In contrast to common approaches using symmetric marginal probability density functions of subband coefficients, experimental manipulations show that the symmetric shape assumption is violated for several texture classes. From this fact, we propose in this paper to exploit this shape property to improve texture characterization. We present Asymmetric Generalized Gaussian density as a model to represent detail subbands resulting from multiscale decomposition. A fast estimation method is presented and closed-form of Kullback-Leibler divergence is provided in order to validate the model into a retrieval scheme. The experimental results indicate that this model achieves higher recognition rates than the conventional approach of using the Generalized Gaussian model where asymmetry was not considered.
Complete list of metadata

Cited literature [8 references]  Display  Hide  Download
Contributor : Nour-Eddine Lasmar Connect in order to contact the contributor
Submitted on : Monday, September 3, 2012 - 5:48:17 PM
Last modification on : Wednesday, January 31, 2018 - 1:46:02 PM
Long-term archiving on: : Tuesday, December 4, 2012 - 3:40:36 AM


Files produced by the author(s)



Nour-Eddine Lasmar, Youssef Stitou, Yannick Berthoumieu. Multiscale skewed heavy tailed model for texture analysis. ICIP - IEEE International Conference on Image Processing, 2009, Cairo, Egypt. pp.2281-2284, ⟨10.1109/ICIP.2009.5414404⟩. ⟨hal-00727111⟩



Record views


Files downloads