Skip to Main content Skip to Navigation
Conference papers

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

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01716919
Contributor : Yannick Berthoumieu Connect in order to contact the contributor
Submitted on : Sunday, February 25, 2018 - 12:35:11 PM
Last modification on : Monday, November 26, 2018 - 1:30:03 PM
Long-term archiving on: : Monday, May 28, 2018 - 5:52:17 PM

File

ICIP_2017.pdf
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

135

Files downloads

450