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Parametric Gaussianization procedure of wavelet coefficients for texture retrieval

Nour-Eddine Lasmar 1, * Youssef Stitou 2 Soufiane Jouini 2 Yannick Berthoumieu 2 Mohamed Najim 2 
* Corresponding author
1 Groupe Signal et Image
IMS - Laboratoire de l'intégration, du matériau au système
Abstract : In this paper, we deal with the problem of feature extraction in content-based image retrieval (CBIR) using statistical approach. A Gaussianization procedure based on parametric density assumptions of steerable pyramid coefficients is proposed. The extraction method of features including the Gaussianization step allows us to limit the order of the statistical model used to characterize the image textures. The performances of the proposed method are analyzed on a database of texture images and compared with the performances of other texture features proposed in previous works.
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Submitted on : Friday, September 14, 2012 - 6:38:22 PM
Last modification on : Wednesday, January 31, 2018 - 1:46:02 PM
Long-term archiving on: : Saturday, December 15, 2012 - 2:30:08 AM


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Nour-Eddine Lasmar, Youssef Stitou, Soufiane Jouini, Yannick Berthoumieu, Mohamed Najim. Parametric Gaussianization procedure of wavelet coefficients for texture retrieval. IEEE ICASSP - International Conference on Acoustics, Speech and Signal Processing, 2008, Las Vegas, United States. pp.749-752, ⟨10.1109/ICASSP.2008.4517718⟩. ⟨hal-00727106⟩



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