Skip to Main content Skip to Navigation
Conference papers

Texture classification using Rao's distance: an EM algorithm on the Poincaré half plane

Abstract : This paper presents a new Bayesian approach to texture classification, yielding enhanced performance in the presence of intraclass diversity. From a mathematical point of view, it specifies an original EM algorithm for mixture estimation on Riemannian manifolds, generalising existing, non probabilistic, clustering analysis methods. For texture classification, the chosen feature space is the Riemannian manifold known as the Poincaré half plane, here denoted H, (this is the set of univariate normal distributions, equipped with Rao's distance). Classes are modelled as finite mixtures of Riemannian priors, (Riemannian priors are probability distributions, recently introduced by the authors, which represent clusters of points in H). During the training phase of classification, the EM algorithm, proposed in this paper, computes maximum likelihood estimates of the parameters of these mixtures. The algorithm combines the structure of an EM algorithm for mixture estimation, with a Riemannian gradient descent, for computing weighted Riemannian centres of mass.
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01229049
Contributor : Lionel Bombrun Connect in order to contact the contributor
Submitted on : Friday, November 20, 2015 - 4:26:27 PM
Last modification on : Wednesday, January 31, 2018 - 1:46:02 PM
Long-term archiving on: : Friday, April 28, 2017 - 3:13:21 PM

File

Said15_ICIP.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01229049, version 1

Citation

Salem Said, Lionel Bombrun, Yannick Berthoumieu. Texture classification using Rao's distance: an EM algorithm on the Poincaré half plane . IEEE International Conference on Image Processing (ICIP), Sep 2015, Québec, Canada. ⟨hal-01229049⟩

Share

Metrics

Record views

134

Files downloads

262