Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2014
Abstract
We propose a novel hybrid metric learning approach to combine multiple heterogenous statistics for robust image set classification. Specifically, we represent each set with multiple statistics – mean, covariance matrix and Gaussian distribution, which generally complement each other for set modeling. However, it is not trivial to fuse them since the mean vector with dd-dimension often lies in Euclidean space RdRd, whereas the covariance matrix typically resides on Riemannian manifold Sym+dSymd+. Besides, according to information geometry, the space of Gaussian distribution can be embedded into another Riemannian manifold Sym+d+1Symd+1+. To fuse these statistics from heterogeneous spaces, we propose a Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to exploit both Euclidean and Riemannian metrics for embedding their original spaces into high dimensional Hilbert spaces and then jointly learn hybrid metrics with discriminant constraint. The proposed method is evaluated on two tasks: set-based object categorization and video-based face recognition. Extensive experimental results demonstrate that our method has a clear superiority over the state-of-the-art methods.
Keywords
Gaussian Mixture Model, Reproduce Kernel Hilbert Space, Symmetric Positive Definite Matrice, Symmetric Positive Definite, Heterogeneous Space
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
Proceedings of the 12th Asian Conference on Computer Vision, Singapore, 2014 November 1-5
Volume
9005
First Page
562
Last Page
577
ISBN
9783319168104
Identifier
10.1007/978-3-319-16811-1_37
Publisher
Springer
City or Country
Switzerland
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons