2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-4.9
Paper Title AFFINE NON-NEGATIVE COLLABORATIVE REPRESENTATION FOR DEEP METRIC LEARNING
Authors Min Zhu, Bao-Di Liu, Weifeng Liu, Kai Zhang, China University of Petroleum (East China), China; Ye Li, Qilu University of Technology (Shandong Academy of Sciences), China; Xiaoping Lu, Haier Industrial Intelligence Institute Co., Ltd, China
SessionMLR-APPL-IP-4: Machine learning for image processing 4
LocationArea D
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we propose a deep metric learning method based on affine non-negative collaborative representation (DML-ANCR) for person and vehicle re-identification. Our method can adaptively generate a non-negative coefficient matrix for support samples per class and fit the query sample with the support samples in the affine subspace. We predict the query sample’s label via the residual between the query sample and optimal fitness. We formulate the affine non-negative collaborative representation learning as a meta-learning problem and present an episode-based approach to learning the best fitness to maximize generalization. Besides, we apply a hard mining strategy to improve the robustness of the metric. In experiments, we also introduce the re-ranking method. Results show our approach has achieved very competitive performance on the widely used person and vehicle re-identification datasets. It surpasses most baseline methods and state-of-the-art methods.