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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.
Cited literature [9 references]
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
Document(s) archivé(s) le : Monday, December 3, 2012 - 2:30:08 AM
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
Document(s) archivé(s) le : Monday, December 3, 2012 - 2:30:08 AM
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