Learning a Mahalanobis distance metric via regularized LDA for scene recognition

Constructing a suitable distance metric for scene recognition is a very challenging task due to the huge intra-class variations. In this paper, we propose a novel framework for learning a full parameter matrix in Mahalanobis metric, where the learning process is formulated as a non-negatively constrained minimization problem in a projected space. To fully capture the structure of scenes, we first apply multiple regularized linear discriminant analysis (LDA) to form a candidate projection pool. Second, we adopt the pairwise squared differences of the projected samples as the learning instances. Finally, the diagonal selection matrix is learned through least squares with non-negative L₂-norm regularization. Experiments on two datasets in scene recognition show the effectiveness and efficiency of our approach.