Contourlet-based texture retrieval using a mixture of generalized gaussian distributions

Authors:
Mohand Saïd Allili;Nadia Baaziz
Affiliations:
Université du Québec en Outaouais, Département d'informatique et d'ingénierie, Gatineau, Québec, Canada;Université du Québec en Outaouais, Département d'informatique et d'ingénierie, Gatineau, Québec, Canada
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Year:
2011

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Abstract

We address the texture retrieval problem using contourletbased statistical representation. We propose a new contourlet distribution modelling using finite mixtures of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of contourlet histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdfs). We propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte-Carlo sampling methods. We show that our approach using a redundant contourlet transform yields better texture discrimination and retrieval results than using other methods of statistical-based wavelet/contourlet modelling.