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Nonnegative discriminative encoded nearest points for image set classification

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Abstract

Image set classification has drawn much attention due to its rich set information. Recently, the most popular set-to-set distance-based representation methods have achieved interesting results by measuring the between-set distance. However, there are two intuitive assumptions, which are largely ignored: (1) The homogeneous samples should have positive contributions to approximate the nearest point in the probe set, while the heterogeneous samples should have no contributions and (2) the learned nearest points in each gallery set should have the lowest correlations. Therefore, this paper presents a novel method called nonnegative discriminative encoded nearest points for image set classification. Specifically, we use two explicit nonnegative constraints to ensure the coding coefficients sparse and discriminative simultaneously. Moreover, we additionally introduce a new class-wise discriminative term to further boost the discriminant ability of different sets. In this way, they can be boosted mutually so that the obtained coding coefficients are beneficial to the purpose of set classification. The results from extensive experiments and comparisons with some state-of-the-art methods on four challenging datasets demonstrate the superiority of our method.

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Acknowledgements

We would like to thank Pengfei Zhu, who shared the source code of RH-ISCRC on their homepage, and to thank Meng Yang, who shared the RNP code. We would also like to thank all the anonymous reviewers who provided substantive suggestions for improving our work.

Funding

This research was supported by the National Natural Science Foundation of China (Grant No. 61673220), the Department of Science and Technology of Sichuan Province (No. ZYF-2018-106), the Sichuan Province Science and Technology Support Program (No. 2018TZDZX0002), and the State Administration for Science, Technology and Industry for National Defense (Nos. JCKY2017209B010 and JCKY2018209B001).

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Authors and Affiliations

  1. Department of National Defence Science and Technology, Southwest University of Science and Technology, Room 606, Building 6A, Mianyang, 621010, China

    Zhenwen Ren & Chao Yang

  2. Department of Computer Science, Nanjing University of Science and Technology, Room 3014, Nanjing, 210094, China

    Zhenwen Ren & Quansen Sun

Authors
  1. Zhenwen Ren
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  2. Quansen Sun
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  3. Chao Yang
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Correspondence to Zhenwen Ren or Quansen Sun.

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Ren, Z., Sun, Q. & Yang, C. Nonnegative discriminative encoded nearest points for image set classification. Neural Comput & Applic 32, 9081–9092 (2020). https://doi.org/10.1007/s00521-019-04419-y

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