Multivariate statistical modeling for texture analysis using wavelet transformsReportar como inadecuado

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1 IMS - Laboratoire de l-intégration, du matériau au système

Abstract : In the framework of wavelet-based analysis, this paper deals with texture modeling for classification or retrieval systems using non-Gaussian multivariate statistical features. We propose a stochastic model based on Spherically Invariant Random Vectors SIRVs joint density function with Weibull assumption to characterize the dependences between wavelet coefficients. For measuring similarity between two texture images, the Kullback-Leibler divergence KLD between the corresponding joint distributions is provided. The evaluation of model performance is carried out in the framework of retrieval system in terms of recognition rate. A comparative study between the proposed model and conventional models such as univariate Generalized Gaussian distribution and Multivariate Bessel K forms MBKF is conducted.

Autor: Nour-Eddine Lasmar - Yannick Berthoumieu -



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