Random linear interpolation data augmentation for person re-identification | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

Random linear interpolation data augmentation for person re-identification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Person Re-Identification (person re-ID) is an image retrieval task which identifies the same person in different camera views. Generally, a good person re-ID model requires a large dataset containing over 100000 images to reduce the risk of over-fitting. Most current handcrafted person re-ID datasets, however, are insufficient for training a learning model with high generalization ability. In addition, the lacking of images with various levels of occlusion is still remaining in most existing datasets. Motivated by these two problems, this paper proposes a new data augmentation method called Random Linear Interpolation that can enlarge the sizes of person re-ID datasets and improve the generalization ability of the learning model. The key enabler of our approach is generating fused images by interpolating pairs of original images. In other words, the innovation of the proposed approach is considering data augmentation between two random samples. Plenty of experimental results demonstrates that the proposed method is effective to improve baseline models. On Market1501 and DukeMTMC-reID datasets, our approach can achieve 92.71% and 82.19% rank-1 accuracy, respectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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. Bai S, Bai X (2016) Sparse contextual activation for efficient visual re-ranking. IEEE Trans Image Process 25(3):1056–1069

    Article  MathSciNet  Google Scholar 

  2. Barbosa IB, Cristani M, Caputo B, Rognhaugen A, Theoharis T (2017) Looking beyond appearances: synthetic training data for deep cnns in re-identification. Computer Vision & Image Understanding

  3. Boroujeni FR, Wang S, Li Z, West N, Stantic B, Yao L, Long G (2018) Trace ratio optimization with feature correlation mining for multiclass discriminant analysis. In: The AAAI conference on artifical intelligence

  4. Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2016) Compound rank- k projections for bilinear analysis. IEEE Transactions on Neural Networks & Learning Systems 27(7):1502–1513

    Article  MathSciNet  Google Scholar 

  5. Chapelle O, Weston J (2011) Vicinal risk minimization. Advances in Neural Information Processing Systems, pp 416–422

  6. Cheng D, Chang X, Liu L, Hauptmann AG, Gong Y, Zheng N (2017) Discriminative dictionary learning with ranking metric embedded for person re-identification. In: Twenty-sixth international joint conference on artificial intelligence, pp 964–970

  7. Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60

    Article  Google Scholar 

  8. Felzenszwalb P, Mcallester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. Cvpr 8:1–8

    Google Scholar 

  9. Geng M, Wang Y, Xiang T, Tian Y (2016) Deep transfer learning for person re-identification. Computer Vision & Image Understanding

  10. Goodfellow IJ, Warde-Farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. Computer Science pp 1319–1327

  11. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. pp 770–778

  12. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification

  13. Hirzer M, Roth PM, Bischof H (2012) Person re-identification by efficient impostor-based metric learning. In: IEEE Ninth international conference on advanced video and signal-based surveillance, pp 203–208

  14. Hirzer M, Roth PM, Stinger M, Bischof H (2012) Relaxed pairwise learned metric for person re-identification. In: European conference on computer vision, pp 780–793

    Chapter  Google Scholar 

  15. Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE Conference on computer vision and pattern recognition, pp 2261–2269

  16. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. pp 448–456

  17. Kodirov E, Xiang T, Fu Z, Gong S (2007) Person re-identification by unsupervised ł1 graph learning. Hydrobiologia 415(11):35–40

    Google Scholar 

  18. Kodirov E, Xiang T, Gong S (2015) Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification. In: British machine vision conference, pp 44.1–44.12

  19. Kreutz-Delgado K, Murray JF, Rao BD, Engan K, Lee TW, Sejnowski TJ (2003) Dictionary learning algorithms for sparse representation. Neural Comput 15(2):349–396

    Article  Google Scholar 

  20. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: International conference on neural information processing systems, pp 1097–1105

  21. Lei Z, Shen J, Liang X, Cheng Z (2016) Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE Transactions on Cybernetics PP (99):1–14

    Google Scholar 

  22. Li J, Wei Y, Liang X, Zhao F, Li J, Xu T, Feng J (2017) Deep attribute-preserving metric learning for natural language object retrieval. In: ACM On multimedia conference, pp 181–189

  23. Li Z, Nie F, Chang X, Yang Y (2017) Beyond trace ratio: weighted harmonic mean of trace ratios for multiclass discriminant analysis. IEEE Transactions on Knowledge & Engineering PP(99):1–1

    Google Scholar 

  24. Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Computer vision and pattern recognition, pp 2197–2206

  25. Lisanti G, Masi I, Bimbo AD (2014) Matching people across camera views using kernel canonical correlation analysis. pp 1–6

  26. Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models

  27. Roth PM, Hirzer M, K?stinger M, Beleznai C, Bischof H (2014) Mahalanobis distance learning for person re-identification. Springer, London

    Chapter  Google Scholar 

  28. Roth PM, Wohlhart P, Hirzer M, Kostinger M, Bischof H (2012) Large scale metric learning from equivalence constraints. In: IEEE Conference on computer vision and pattern recognition, pp 2288–2295

  29. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Computer Science

  30. Sun Y, Zheng L, Deng W, Wang S (2017) Svdnet for pedestrian retrieval. In: IEEE International conference on computer vision, pp 3820–3828

  31. Tanner MA, Wong WH (1987) The calculation of posterior distributions by data augmentation. Publ Am Stat Assoc 82(398):528–540

    Article  MathSciNet  Google Scholar 

  32. Torralba A, Fergus R, Freeman WT (2008) 80 Million Tiny Images: a Large Data Set for Nonparametric Object and Scene Recognition. IEEE Computer Society

  33. Xiong F, Gou M, Camps O, Sznaier M (2014) Person re-identification using kernel-based metric learning methods. In: European conference on computer vision, pp 1–16

    Google Scholar 

  34. Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. Computer Science

  35. Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. In: Computer vision and pattern recognition, pp 1239–1248

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

  37. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past present and future

  38. Zheng Z, Zheng L, Yang Y (2017) Pedestrian alignment network for large-scale person re-identification

  39. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro. pp 3774–3782

  40. Zhong Z, Zheng L, Cao D, Li S (2017) Re-ranking person re-identification with k-reciprocal encoding. pp 3652–3661

  41. Zhong Z, Zheng L, Kang G, Li S, Yang Y (2017) Random erasing data augmentation

  42. Zhu L, Huang Z, Liu X, He X, Sun J, Zhou X (2017) Discrete multimodal hashing with canonical views for robust mobile landmark search. IEEE Trans Multimedia 19(9):2066–2079

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Program No. 61702415, No.61502387), Natural Science Basic Research Plan in Shaanxi Province of China (Program No.2017JM6056,2016JQ6029) and Talent Support Project of Science Association in Shaanxi Province (Program No. 20180108).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xian, China

    Zhi Li, Jun Guo, Wenli Jiao, Pengfei Xu, Baoying Liu & Xiaowei Zhao

Authors
  1. Zhi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jun Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wenli Jiao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Pengfei Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Baoying Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Xiaowei Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Pengfei Xu.

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

Li, Z., Guo, J., Jiao, W. et al. Random linear interpolation data augmentation for person re-identification. Multimed Tools Appl 79, 4931–4947 (2020). https://doi.org/10.1007/s11042-018-7071-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-7071-5

Keywords