Feature extraction using two-dimensional maximum embedding difference

In this paper we propose a novel method combining graph embedding and difference criterion techniques for image feature extraction, namely two-dimensional maximum embedding difference (2DMED). This method directly extracts the optimal projective vectors from 2D image matrices by simultaneously considering characteristic that is the intra-class compactness graph, the margin graph and inter-class separability graph, respectively. In this method, it is not necessary to convert the image matrix into high-dimensional image vector so that much computational time would be saved. In addition, the proposed method preserves the manifold reconstruction relationships in the low-dimensional subspace. Experimental results on the ORL, Yale face and USPS database show the effectiveness of the proposed method.