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Texture classification based on the generalized gamma distribution and the dual tree complex wavelet transform

Abstract : This paper deals with stochastic texture modeling for classification issue. A generic stochastic model based on three-parameter Generalized Gamma (GG) distribution func-tion is proposed. The GG modeling offers more flexibility pa-rameterization than other kinds of heavy-tailed density devoted to wavelet empirical histograms characterization. Moreover, Kullback-leibler divergence is chosen as similarity measure between textures. Experiments carried out on Vistex texture database show that the proposed approach achieves good classification rates.
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https://hal.inria.fr/hal-00727115
Contributor : Nour-Eddine Lasmar <>
Submitted on : Sunday, September 2, 2012 - 12:54:12 AM
Last modification on : Friday, August 9, 2019 - 3:10:32 PM
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Ahmed Drissi El Maliani, Nour-Eddine Lasmar, Mohammed El Hassouni, Yannick Berthoumieu. Texture classification based on the generalized gamma distribution and the dual tree complex wavelet transform. ISIVC - International Symposium on Image/Video Communications over fixed and mobile networks, 2010, Rabat, Morocco. pp.1-4, ⟨10.1109/ISVC.2010.5656257⟩. ⟨hal-00727115⟩

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