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Tensor-driven low-rank discriminant analysis for image set classification

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Abstract

Classification based on image sets has recently attracted great interest in computer vision community. In this paper, we proposed a transductive Tensor-driven Low-rank Discriminant Analysis (TLRDA) model for image set classification, in which the tensor-driven low-rank approximation and the discriminant graph embedding are integrated to improve the representativeness of image sets. In addition, we develop an iterative shrinkage thresholding algorithm to better optimize the objective function of the proposed TLRDA. Experiments on seven publicly available datasets demonstrate that our proposed method is guaranteed to converge within a small number of iterations during the training procedure and obtains promising results compared with state-of-the-art methods.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Jing Zhang, Zhengnan Li, Peiguang Jing & Yuting Su

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Ye Liu

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  1. Jing Zhang
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  2. Zhengnan Li
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  4. Ye Liu
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Correspondence to Peiguang Jing.

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Zhang, J., Li, Z., Jing, P. et al. Tensor-driven low-rank discriminant analysis for image set classification. Multimed Tools Appl 78, 4001–4020 (2019). https://doi.org/10.1007/s11042-017-5173-0

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