Gaussian Copula Multivariate Modeling for Image Texture Retrieval Using Wavelet Transforms

Abstract : In the framework of texture image retrieval, a new family of stochastic multivariate modeling is proposed based on Gaussian Copula and wavelet decompositions. We take advantage of copula paradigm which makes it possible to separate dependency structure from marginal behavior. We introduce two new multivariate models using respectively generalized Gaussian and Weibull density. These models capture both the subband marginal distributions and the correlation between wavelet coefficients. We derive, as a similarity measure, a closed form solution of the Jeffrey divergence between Gaussian Copula based multivariate models. Experimental results on the well-known databases show significant improvements in retrieval rates using the proposed method compared to the best known state-of-the-art approaches.
Liste complète des métadonnées

Littérature citée [57 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00727127
Contributeur : Nour-Eddine Lasmar <>
Soumis le : mardi 25 mars 2014 - 18:24:44
Dernière modification le : jeudi 15 mars 2018 - 09:36:02
Document(s) archivé(s) le : mercredi 25 juin 2014 - 13:05:49

Fichier

TIP_201403_single_column.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Citation

Nour-Eddine Lasmar, Yannick Berthoumieu. Gaussian Copula Multivariate Modeling for Image Texture Retrieval Using Wavelet Transforms. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2014, 23 (5), pp.2246 - 2261. 〈http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6777560〉. 〈10.1109/TIP.2014.2313232〉. 〈hal-00727127v3〉

Partager

Métriques

Consultations de la notice

437

Téléchargements de fichiers

540