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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Communication dans un congrès
ISIVC - International Symposium on Image/Video Communications over fixed and mobile networks, 2010, Rabat, Morocco. IEEE, pp.1-4, 2010, 〈10.1109/ISVC.2010.5656257〉
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Dernière modification le : mercredi 31 janvier 2018 - 13:46:02
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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. IEEE, pp.1-4, 2010, 〈10.1109/ISVC.2010.5656257〉. 〈hal-00727115〉

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