Sparse Approximated Nearest Points for Image Set Classification

Classification based on image sets has recently attracted great research interest as it holds more promise than single image based classification. In this paper, we propose an efficient and robust algorithm for image set classification. An image set is represented as a triplet: a number of image samples, their mean and an affine hull model. The affine hull model is used to account for unseen appearances in the form of affine combinations of sample images. We introduce a novel between-set distance called Sparse Approximated Nearest Point (SANP) distance. Unlike existing methods, the dissimilarity of two sets is measured as the distance between their nearest points, which can be sparsely approximated from the image samples of their respective set. Different from standard sparse modeling of a single image, this novel sparse formulation for the image set enforces sparsity on the sample coefficients rather than the model coefficients and jointly optimizes the nearest points as well as their sparse approximations. A convex formulation for searching the optimal SANP between two sets is proposed and the accelerated proximal gradient method is adapted to efficiently solve this optimization. Experimental evaluation was performed on the Honda, MoBo and Youtube datasets. Comparison with existing techniques shows that our method consistently achieves better results.