Feature extraction using two-dimensional neighborhood margin and variation embedding

Authors:
Quanxue Gao;Xiujuan Hao;Qijun Zhao;Weiguo Shen;Jingjie Ma
Affiliations:
State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, PR China;State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, PR China;Department of Computer Science and Engineering, Michigan State University, East Lansing, USA;State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, PR China;State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, PR China
Venue:
Computer Vision and Image Understanding
Year:
2013

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Abstract

In this paper, we introduce a novel linear discriminant approach called Two-Dimensional Neighborhood Margin and Variation Embedding (2DNMVE), which explicitly considers the modes of variability among nearby images and the discriminating information. To be specific, we construct an adjacency graph to model the intra-class variation, which characterizes the modes of variability of the face images, of the values of face images from the same class, and inter-class variation which encodes the discriminating information, and then incorporate the modes of variability and discriminating information into the objective function of dimensionality reduction. Thus, 2DNMVE is robust to intra-class variation and has better generalization capability on testing data. Experiments on four face databases show the effectiveness of the proposed approach.