Skip to main content
SpringerLink
Log in

Person Re-Identification Based on Graph Relation Learning

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Person re-identification (Re-ID) aims to find the person across non-overlapping camera views in public places. Existing methods are getting better and better at learning finer pedestrian details, but at the same time they all ignore the relations between these details, which makes some details that are critical to discrimination unable to play a key role in decision-making. To solve this problem, we propose a graph relation learning method for person re-identification. Firstly, we use graph structure to build the relation graph, and then use the weight operation to get the relation vertices that can receive suggestions from other details. Finally, by using the collaborative training scheme to train relation vertices and regional global average features, our model can learn the relation information. Extensive experiments show that the proposed method can effectively improve the discriminative ability of the model, enhance the role of neglected important clues in decision-making, and achieve state-of-the-arts performance on the more challenging CUHK03-NP dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Martin K, Martin H, Paul W, Peter MR, Horst B (2012) Large scale metric learning from equivalence constraints. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 2288–2295

  2. Yang Y, Yang J, Yan J, Liao S, Yi D, Li SZ (2014) Salient color names for person re-identification. In: European conference on computer vision. Springer, pp 536–551

  3. Chen Y, Shin H (2020) Multispectral image fusion based pedestrian detection using a multilayer fused deconvolutional single-shot detector. JOSA A 37(5):768–779

    Article  Google Scholar 

  4. Zhang Y, Wang X, Bi X, Tao D (2018) A light dual-task neural network for haze removal. IEEE Signal Process Lett 25(8):1231–1235

    Article  Google Scholar 

  5. Wan X, Zhao C (2017) Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images. JOSA A 34(6):1011–1020

    Article  Google Scholar 

  6. Lyu J, Bi X, Ling SH (2020) Multi-level cross residual network for lung nodule classification. Sensors 20(10):2837

    Article  Google Scholar 

  7. Jun Yu, Yang X, Gao F, Tao D (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024

    Google Scholar 

  8. Yu Z, Yu J, Fan J, Tao D (2017) Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In: Proceedings of the IEEE international conference on computer vision, pp 1821–1830

  9. Jun Y, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell

  10. Jun Y, Yao J, Zhang J, Zhou Y, Dacheng T (2020) Single-pixel reconstruction for one-stage instance segmentation. IEEE Transactions on Cybernetics, Sprnet

  11. Yang F, Wei Y, Zhou Y, Shi H, Huang G, Wang X, Yao Z, Huang T (2019) Horizontal pyramid matching for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8295–8302

  12. Zhou S, Wang F, Huang Z, Wang J (2019) Discriminative feature learning with consistent attention regularization for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 8040–8049

  13. Yang W, Huang H, Zhang Z, Chen X, Huang K, Zhang S (2019) Towards rich feature discovery with class activation maps augmentation for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1389–1398

  14. Chen B, Deng W, Hu J (2019) Mixed high-order attention network for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 371–381

  15. Zheng M, KaranamS, Wu Z, Radke RJ (2019) Re-identification with consistent attentive siamese networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5735–5744

  16. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp 480–496

  17. Wang Z, Jiang J, Yang W, Ye M, Bai X, Satoh S (2019) Learning sparse and identity-preserved hidden attributes for person re-identification. IEEE Trans Image Process 29(1):2013–2025

    Google Scholar 

  18. Qian X, Fu Y, Xiang T, Wang W, Qiu J, Wu Y, Jiang Y-G, Xue X (2018) Pose-normalized image generation for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 650–667

  19. Liu J, Ni B, Yan Y, Zhou P, Cheng S, Hu J (2018) Pose transferrable person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4099–4108

  20. Kalayeh MM, Basaran E, Gökmen M, Kamasak ME, Shah M (2018) Human semantic parsing for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1062–1071

  21. Xu J, Zhao R, Zhu F, Wang H, Ouyang W (2018) Attention-aware compositional network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2119–2128

  22. Kang B-N, Kim Y, Jun B, Kim D (2019) Attentional feature-pair relation networks for accurate face recognition. In: Proceedings of the IEEE international conference on computer vision, pp 5472–5481

  23. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  24. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  25. Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400

  26. Song G, Chai W (2018) Collaborative learning for deep neural networks. In: Advances in neural information processing systems, pp 1832–1841

  27. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  28. Zhong Z, Zheng L, Cao D, Li S (2017) Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1318–1327

  29. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 152–159

  30. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark. In: Proceedings of the IEEE international conference on computer vision, pp 1116–1124

  31. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: Proceedings of the IEEE international conference on computer vision, pp 3754–3762

  32. Wang X, Doretto G, Sebastian T, Rittscher J, Tu P (2007) Shape and appearance context modeling. In: 2007 IEEE 11th international conference on computer vision. IEEE, pp 1–8

  33. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  34. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  35. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

  36. Ma N, Zhang X, Zheng H-T, Sun J (2018) Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp 116–131

Download references

Author information

Authors and Affiliations

  1. The College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Hao Wang

  2. The School of Information Engineering, Minzu University of China, Beijing, 100081, China

    Xiaojun Bi

Authors
  1. Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaojun Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaojun Bi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Bi, X. Person Re-Identification Based on Graph Relation Learning. Neural Process Lett 53, 1401–1415 (2021). https://doi.org/10.1007/s11063-021-10446-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10446-5

Keywords

Search

Navigation