Multivariate statistical modeling for texture analysis using wavelet transforms

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.
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Communication dans un congrès
IEEE ICASSP - IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, Dallas, United States. IEEE, pp.790-793, 2010, 〈10.1109/ICASSP.2010.5494963〉
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Contributeur : Nour-Eddine Lasmar <>
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Dernière modification le : mercredi 31 janvier 2018 - 13:46:02
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Nour-Eddine Lasmar, Yannick Berthoumieu. Multivariate statistical modeling for texture analysis using wavelet transforms. IEEE ICASSP - IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, Dallas, United States. IEEE, pp.790-793, 2010, 〈10.1109/ICASSP.2010.5494963〉. 〈hal-00727113〉

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