Contextual constraints based linear discriminant analysis

Authors:
Zhen Lei;Stan Z. Li
Affiliations:
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu, Beijing 100190, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu, Beijing 100190, China
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

Linear feature extraction methods such as LDA have achieved great success in pattern recognition and image processing area. For most existing methods, the image data is usually transformed into a vector representation and the contextual information among pixels is not exploited. However, image data distribute sparsely in high-dimension feature space and the dependence among neighboring pixels is important to represent a natural image. Therefore, in this paper, we propose a novel image contextual constraint based linear discriminant analysis (CCLDA) method by taking into account the pixel dependence of an image in subspace learning process. In this way, a more discriminative subspace could be learned especially in the case of small sample size. Extensive experiments on ORL, Extended Yale-B, PIE and FRGC databases validate the efficacy of the proposed method.