Multi-task image set classification via joint representation with class-level sparsity and intra-task low-rankness

摘要

Image set classification has recently attracted great attention due to its widespread applications in computer vision and pattern recognition. The great challenges lie in effectively and efficiently measuring the similarity among image sets with high inter-class ambiguity and large intra-class variability. In this paper, we propose a joint representation based approach to image set classification, in which class-level sparse and globally low rank constraints are imposed on the representation coefficients to embody inter-set discrimination and intra-set commonality respectively. Furthermore, sometimes the small size of image sets or improper usage of a single kind of features causes useful information limited and lacking in discriminability. To address this problem, we extend the traditional image set classification to a multi-task version, i.e., modify the proposed model to incorporate multiple kinds of features. Fortunately, on the total multi-task representation coefficients, both the total class-level sparsity and the intra-task low-rankness constraints still apply. The proposed method is optimized as a non-smooth convex optimization problem by employing an alternating optimization technique. Experiments on five public datasets demonstrate that the proposed method surpasses existing joint representation models with various regularizations for image set classification and compares favorably with other state-of-the-art methods. (c) 2018 Elsevier B.V. All rights reserved.