Contextual constraints based linear discriminant analysis

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.

Research highlights
► 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, the representation of the proposed method is expected to be more discriminant and robust for image classification.
► Two contextual constraint examples named hard and soft constraints are proposed.
► The relationship of the proposed method with LDA and 2D-LDA are analyzed.
► Extensive experiments on ORL, Extended Yale-B, PIE and FRGC databases validate the efficacy of the proposed method.