Local Discriminative Orthogonal Rank-One Tensor Projection for image feature extraction

This paper develops a Local Discriminative Orthogonal Rank-One Tensor Projection (LDOROTP) technique for image feature extraction. The goal of LDOROTP is to learn a compact feature for images meanwhile endow the feature with prominent discriminative ability. LDOROTP achieves the goal through a serial of rank-one tensor projections with orthogonal constraints. To seek the optimal projections, LDOROTP carries out local discriminant analysis, but differs from the previous works on two aspects: (1)the local neighborhood consists of all the samples of the same class and partial local samples from different classes; (2)a novel weighting function is designed to encode the local discriminant information. The criterion of LDOROTP is built on the trace differences of matrices rather than the trace ratio, so the awkward problem of singular matrix do not emerges. Besides, LDOROTP benefits from an efficient and stable iterative scheme of solution and a data preprocessing called GLOCAL tensor representation. LDOROTP is evaluated on face recognition application on two benchmark databases: Yale and PIE, and compared with several popular projection techniques. Experimental results suggest that the proposed LDOROTP provides a supervised image feature extraction approach of powerful pattern revealing capability.