Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images

Résumé

This paper introduces a novel local model for the classification of covariance matrices: the co-occurrence matrix of covariance matrices. Contrary to state-of-the-art models (BoRW, R-VLAD and RFV), this local model exploits the spatial distribution of the patches. Starting from the generative mixture model of Riemannian Gaussian distributions , we introduce this local model. An experiment on texture image classification is then conducted on the VisTex and Outex_TC000_13 databases to evaluate its potential.
Fichier principal
Vignette du fichier
Ilea17_GSI.pdf (859.41 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01629171 , version 1 (06-11-2017)

Identifiants

  • HAL Id : hal-01629171 , version 1

Citer

Ioana Ilea, Lionel Bombrun, Salem Said, Yannick Berthoumieu. Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images. 3rd conference on Geometric Science of Information, Nov 2017, Paris, France. ⟨hal-01629171⟩
82 Consultations
340 Téléchargements

Partager

Gmail Facebook X LinkedIn More