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

Ahmed Drissi El Maliani 1 Nour-Eddine Lasmar 2 Mohammed El Hassouni 3 Yannick Berthoumieu 2
1 LRIT - Laboratoire de Recherche Informatique et Télécommunications
2 IMS - Laboratoire de l'intégration, du matériau au système
3 DESTEC - Departement des Sciences et Techniques de la Communication
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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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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