Two-dimensional bilinear preserving projections for image feature extraction and classification

Two-dimensional locality preserving projections (2DLPP) was recently proposed to extract features directly from image matrices based on locality preserving criterion. A significant drawback of 2DLPP is that it only works on one direction (left or right) to reduce the dimensionality of the image matrices and thus too many coefficients are needed for image representation in low-dimensional subspace. In this paper, we propose a novel method called two-dimensional bilinear preserving projections (2DBPP) for image feature extraction. We generalized the image-based (2D-based) feature extraction techniques into bilinear cases, in which 2DLPP is a special case of our proposed method. In order to obtain the bilinear projections, we proposed an iteration method by solving the corresponding generalized eigen-equations. Moreover, analyses show that 2DBPP has stronger locality preserving abilities than 2DLPP. By using the label information and defining different local neighborhood graphs, the proposed framework is further extended to supervised case. Experiments on three databases show that 2DBPP and its supervised extension are superior to some other image-based state-of-the-art techniques.