Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2014

Abstract

In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of the manifold. The final distance metric is then learned by pursuing multiple transformations from the Hilbert space and the original Euclidean space (or its corresponding Hilbert space) to a common Euclidean subspace, where classical Euclidean distances of transformed heterogeneous points can be measured. Extensive experiments clearly demonstrate the superiority of our proposed approach over the state-of-the-art methods.

Keywords

Euclidean-to-Riemannian metric learning; point-to-set classification

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, June 23-28

First Page

1677

Last Page

1684

ISBN

9781479951178

Identifier

10.1109/CVPR.2014.217

Publisher

IEEE Computer Society

City or Country

New York

Download

Share

COinS