We address the texture retrieval problem using contourlet-based 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. © 2011 Springer-Verlag.
CITATION STYLE
Allili, M. S., & Baaziz, N. (2011). Contourlet-based texture retrieval using a mixture of generalized Gaussian distributions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6855 LNCS, pp. 446–454). https://doi.org/10.1007/978-3-642-23678-5_53
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