Paper Info
Title
Regularized Graph-Embedded Covariance Discriminative Learning For Image Set Classification
Abstract
Riemannian manifold has attracted an increasing amount of attention for visual classification tasks, especially for video or image set classification. Covariance matrices are the natural second-order statistics of image sets. However, nonsingular covariance matrices, known as symmetric positive defined (SPD) matrices, lie on the non-Euclidean Riemannian manifold (SPD manifold). Covariance discriminative learning (CDL) is an effective discriminative learning method that employs the Riemannian manifold in the SPD kernel space. However, in practice, the discriminative learning of CDL often suffers from the problems of poor generalization and overfitting caused by a finite number of training samples and noise corruption. Hence, we propose to address these problems by importing eigenspectrum regularization and graphem-bedded frameworks. Discriminative learning with SPD manifold is generalized by the graph-embedded framework, which combines with eigenspectrum regularization in the SPD kernel space. Three local Laplacian graphs of graph-embedded framework and two eigenspectrum regularized models are incorporated to the proposed method. Comprehensive mathematical deduction of the proposed method is depicted with the "kernel tricks." Experimental results on set-based face recognition and object categorization tasks reveal the effectiveness of the proposed method. (C) The Authors.
Year
DOI
Venue
2020
10.1117/1.JEI.29.4.043018
JOURNAL OF ELECTRONIC IMAGING
Keywords
DocType
Volume
symmetric positive defined manifold, covariance discriminative learning, eigenspectrum regularization, graph-embedded framework, image set classification
Journal
29
Issue
ISSN
Citations 
4
1017-9909
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Hengliang Tan172.44
Ying Gao200.34
Jiao Du36810.21
Shuo Yang400.34