Classification approach based on the product of riemannian manifolds from Gaussian parametrization space - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Classification approach based on the product of riemannian manifolds from Gaussian parametrization space

Résumé

This paper presents a novel framework for visual content classification using jointly local mean vectors and covari-ance matrices of pixel level input features. We consider local mean and covariance as realizations of a bivariate Riemannian Gaussian density lying on a product of submanifolds. We first introduce the generalized Mahalanobis distance and then we propose a formal definition of our product-spaces Gaussian distribution on R m × SPD(m). This definition enables us to provide a mixture model from a mixture of a finite number of Riemannian Gaussian distributions to obtain a tractable descriptor. Mixture parameters are estimated from training data by exploiting an iterative Expectation-Maximization (EM) algorithm. Experiments in a texture classification task are conducted to evaluate this extended modeling on several color texture databases, namely popular Vistex, 167-Vistex and CUReT. These experiments show that our new mixture model competes with state-of-the-art on the experimented datasets.
Fichier principal
Vignette du fichier
ICIP_2017.pdf (290.78 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01716919 , version 1 (25-02-2018)

Identifiants

Citer

Yannick Berthoumieu, Lionel Bombrun, Christian Germain, Salem Said. Classification approach based on the product of riemannian manifolds from Gaussian parametrization space. 2017 IEEE International Conference on Image Processing (ICIP), Sep 2017, Beijing, France. ⟨10.1109/ICIP.2017.8296272⟩. ⟨hal-01716919⟩
41 Consultations
182 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More