Skip to main content
SpringerLink
Log in

QRKISS: A Two-Stage Metric Learning via QR-Decomposition and KISS for Person Re-Identification

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Person re-identification is a challenging task in the field of intelligent video surveillance because there are wide variations between pedestrian images. As a classical metric learning method, Keep It Simple and Straightforward (KISS) has shown good performance for person re-identification. However, when the dimension of data is high, the KISS method may perform poorly because of small sample size problem. A common solution to this problem is to apply dimensionality reduction technologies to original data before the KISS metric learning, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In this paper, to learn a discriminant and robust metric, we propose a novel two-stage metric learning via QR-Decomposition and KISS, named QRKISS. The first stage of QRKISS is to project original data into a lower dimensional space by QR decomposition. In this lower dimensional space, the trace of the covariance matrix of interpersonal differences can reach maximum. Based on KISS method, the second stage of QRKISS obtains a Mahalanobis matrix in the low-dimension space. We conduct thorough validation experiments on the VIPeR, PRID 450S and CUHK01 datasets, which demonstrate that QRKISS method is better than other KISS-based metric learning methods and achieves state-of-the-art performance.

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Vezzani R, Baltieri D, Cucchiara R (2013) People reidentification in surveillance and forensics: a survey. ACM Comput Surv CSUR 46(2):29

    Google Scholar 

  2. Bedagkar-Gala A, Shah SK (2014) A survey of approaches and trends in person re-identification. Image Vis Comput 32(4):270–286

    Article  Google Scholar 

  3. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984

  4. Plantinga A (1961) Things and persons. Rev Metaphys 14(3):493–519

  5. Yang Y, Yang J, Yan J et al (2014) Salient color names for person re-identification. In: European conference on computer vision. Springer, Cham, pp 536–551

  6. Liao S, Hu Y, Zhu X et al (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2197–2206

  7. Wu S, Chen Y C, Li X et al (2016) An enhanced deep feature representation for person re-identification. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp 1–8

  8. Chen YC, Zheng WS, Lai J (2015) Mirror representation for modeling view-specific transform in person re-identification. In: Twenty-fourth international joint conference on artificial intelligence

  9. Matsukawa T, Okabe T, Suzuki E et al (2016) Hierarchical gaussian descriptor for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1363–1372

  10. Shen F, Yang Y, Li Liu, Liu W, Tao D, Shen H (2017) Asymmetric binary coding for image search. IEEE Trans Multimed TMM 19(9):2022–2032

    Article  Google Scholar 

  11. Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10(Feb):207–244

    MATH  Google Scholar 

  12. Koestinger M, Hirzer M, Wohlhart P et al (2012) Large scale metric learning from equivalence constraints. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2288–2295

  13. Pedagadi S, Orwell J, Velastin S et al (2013) Local fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3318–3325

  14. Roth P M, Hirzer M, Koestinger M et al (2014) Mahalanobis distance learning for person re-identification. In: Person re-identification. Springer London, pp 247–267

  15. Yang L, Jin R (2006) Distance metric learning: a comprehensive survey. Mich State Univ 2(2):1–51

  16. Shen F, Shen C, Shi Q et al (2015) Hashing on nonlinear manifolds. IEEE Trans Image Process 24(6):1839–1851

    Article  MathSciNet  MATH  Google Scholar 

  17. Shen F, Zhou X, Yang Y et al (2016) A fast optimization method for general binary code learning. IEEE Trans Image Process 25(12):5610–5621

    Article  MathSciNet  MATH  Google Scholar 

  18. Lai Z, Wong W, Xu Y, Zhao C, Sun M (2014) Sparse alignment for robust tensor learning. IEEE Trans Neural Netw Learn Syst 25(10):1779–1792

    Article  Google Scholar 

  19. Li W, Zhao R, Xiao T et al (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

  20. Varior RR, Shuai B, Lu J et al (2016) A siamese long short-term memory architecture for human re-identification. In: European conference on computer vision. Springer, Berlin, pp 135–153

  21. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Article  Google Scholar 

  22. Mika S, Ratsch G, Weston J et al (1999) Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX, 1999. Proceedings of the 1999 IEEE signal processing society workshop. IEEE, pp 41–48

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

  24. Mignon A, Jurie F (2012) PCCA: a new approach for distance learning from sparse pairwise constraints. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR), pp 2666–2672

  25. Li Z, Chang S, Liang F et al (2013) Learning locally-adaptive decision functions for person verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3610–3617

  26. Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1239–1248

  27. Lisanti G, Masi I, Del Bimbo A (2014) Matching people across camera views using kernel canonical correlation analysis. In: Proceedings of the international conference on distributed smart cameras. ACM, p 10

  28. Wang W, Taalimi A, Duan K et al (2016) Learning patch-dependent kernel forest for person re-identification. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1–9

  29. Lisanti G, Karaman S, Masi I (2017) Multichannel-kernel canonical correlation analysis for cross-view person reidentification. ACM Trans Multi Comput Commun Appl TOMM 13(2):13

    Google Scholar 

  30. Liu H, Qi M, Jiang J (2015) Kernelized relaxed margin components analysis for person re-identification. IEEE Signal Process Lett 22(7):910–914

    Article  Google Scholar 

  31. Tao D, Jin L, Wang Y et al (2013) Person re-identification by regularized smoothing kiss metric learning. IEEE Trans Circuits Syst Video Technol 23(10):1675–1685

    Article  Google Scholar 

  32. Tao D, Guo Y, Song M et al (2016) Person re-identification by dual-regularized kiss metric learning. IEEE Trans Image Process 25(6):2726–2738

    Article  MathSciNet  MATH  Google Scholar 

  33. Liao S, Zhao G, Kellokumpu V et al (2010) Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 1301–1306. IEEE

  34. Davis JV, Kulis B, Jain P et al (2007) Information-theoretic metric learning. In: Proceedings of the 24th international conference on machine learning, pp 209–216

  35. Liong V E, Lu J, Ge Y (2014) Regularized Bayesian metric learning for person re-identification. In: ECCV workshops (3), pp 209–224

  36. Golub GH, Van Loan CF (2012) Matrix computations, vol 3. JHU Press, Baltimore

    MATH  Google Scholar 

  37. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of the IEEE international workshop on performance evaluation for tracking and surveillance (PETS), vol 3, No 5. Citeseer, pp 1–7

  38. Li W, Zhao R, Wang X (2012) Human reidentification with transferred metric learning. In: Asian conference on computer vision. Springer, Berlin, pp 31–44

  39. Paisitkriangkrai S, Shen C, van den Hengel A (2015) Learning to rank in person re-identification with metric ensembles. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1846–1855

  40. Liao S, Li SZ (2015) Efficient psd constrained asymmetric metric learning for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3685–3693

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their critical and constructive comments and suggestions. This work is supported by China National Natural Science Foundation under Grant No. 61673299, 61203247 and The Hong Kong Polytechnic University (Project account: G-UC42). This work is also partially supported by China National Natural Science Foundation under Grant No. 61573259, 61573255, 61375012. It is also supported by the Fundamental Research Funds for the Central Universities (Grant No. 0800219327). It is also partially supported by Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) and by the Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education under Grant No. 30920130122005. It is also partially supported by the program of Further Accelerating the Development of Chinese Medicine Three Year Action of Shanghai Grant No. ZY3-CCCX-3-6002.

Author information

Author notes

  1. Cairong Zhao and Yipeng Chen have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China

    Cairong Zhao, Yipeng Chen, Zhihua Wei & Duoqian Miao

  2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, China

    Cairong Zhao

  3. College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China

    Xinjian Gu

Authors
  1. Cairong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yipeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihua Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Duoqian Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinjian Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Cairong Zhao or Zhihua Wei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, C., Chen, Y., Wei, Z. et al. QRKISS: A Two-Stage Metric Learning via QR-Decomposition and KISS for Person Re-Identification. Neural Process Lett 49, 899–922 (2019). https://doi.org/10.1007/s11063-018-9820-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9820-x

Keywords

Search

Navigation