Low-rank regularized tensor discriminant representation for image set classification

Abstract Although image set classification has attracted great attention in computer vision and pattern recognition communities, however, learning a compact and discriminative representation is still a challenge. In this paper, we present a novel tensor discriminant representation learning method to better solve the image classification task. Specifically, we first exploit the advantages of Grassmann manifold and tensor to model image sets as a high-order tensor. We then propose a transductive low-rank regularized tensor discriminant representation algorithm referred as LRRTDR to learn more intrinsic representations for image sets. Our proposed LRRTDR mainly contains two components: low-rank tensor embedding and discriminant graph embedding. The low-rank tensor embedding is to learn the lowest-rank representation from a low-dimensional subspace, which is spanned by a set of latent basis matrices. The discriminant graph embedding is to further enhance the discriminant ability of the learned representations under the graph embedding framework. To solve the optimization problem of LRRTDR, we develop an alternating direction scheme based on Iterative Shrinkage Thresholding Algorithm (ISTA). Experimental results on five publicly available datasets demonstrate that our proposed algorithm not only converges with few iterations, but also achieves better accuracy compared with state-of-the-art methods.