Subspace representation based on pairwise linear regression for large scale image set classification

Abstract

In recent years, image set classification has attracted the attention of many scholars. It is mainly used in complex real scene classification, such as network short video classification, monitoring record classification, and multi-view camera network classification. Pairwise linear regression classification (PLRC) creatively introduces the unrelated subspace to increase the discriminative information, and it achieves a demonstrated better performance on image set classification. However, in the face of large scale image set classification, PLRC is not competent. One possible reason is that it is affected by too many outliers. In order to solve the problem of large scale image set classification, this paper propose two methods. Specifically, one using sparse subspace clustering to mine discriminative features, the other using the self-expressive function to extract exemplar from one image set, both of two methods using PLRC to finish the final classification task, which is not only conductive to reduce the impact of outliers, but also cuts down the computational burden of PLRC. Extensive experiments on two well-known databases prove that the performance of proposed algorithms is better than that of PLRC and several state-of-the-art classifiers.