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Robust dictionary learning with graph regularization for unsupervised person re-identification

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Abstract

Most existing approaches for person re-identification are designed in a supervised way, undergoing a prohibitively high labeling cost and poor scalability. Besides establishing effective similarity distance metrics, these supervised methods usually focus on constructing discriminative and robust features, which is extremely difficult due to the significant viewpoint variations. To overcome these challenges, we propose a novel unsupervised method, termed as Robust Dictionary Learning with Graph Regularization (RDLGR), which can guarantee view-invariance through learning a dictionary shared by all the camera views. To avoid the significant degradation of performance caused by outliers, we employ a capped l 2,1-norm based loss to make our model more robust, addressing the problem that traditional quadratic loss is known to be easily dominated by outliers. Considering the lack of labeled cross-view discriminative information in our unsupervised method, we further introduce a cross-view graph Laplacian regularization term into the framework of dictionary learning. As a result, the geographical structure of original data space can be preserved in the learned latent subspace as discriminative information, making it possible to further boost the matching accuracy. Extensive experimental results over four widely used benchmark datasets demonstrate the superiority of the proposed model over the state-of-the-art methods.

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References

  1. An L, Chen X, Yang S, Bhanu B (2016) Sparse representation matching for person re-identification. Inf Sci 355:74–89

    Article  Google Scholar 

  2. Bird ND, Masoud O, Papanikolopoulos NP, Isaacs A (2005) Detection of loitering individuals in public transportation areas. IEEE Trans Intell Trans Syst 6(2):167–177

    Article  Google Scholar 

  3. Cai D, He X, Han J, Huang TS (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548–1560

    Article  Google Scholar 

  4. Chang X, Nie F, Yang Y, Zhang C, Huang H (2016) Convex sparse pca for unsupervised feature learning. ACM Trans Knowl Discov Data 11(1):3

    Article  Google Scholar 

  5. Chang X, Yang Y (2016) Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2016.2582746

    Article  MathSciNet  Google Scholar 

  6. Chang X, Yu YL, Yang Y, Xing EP (2016) Semantic pooling for complex event analysis in untrimmed videos. IEEE Trans Pattern Anal Mach Intell 39(8):1617–1632

    Article  Google Scholar 

  7. Chen D, Yuan Z, Chen B, Zheng N (2016) Similarity learning with spatial constraints for person re-identification. In: CVPR

  8. Cheng DS, Cristani M, Stoppa M, Bazzani L, Murino V (2011) Custom pictorial structures for re-identification. In: BMVC

  9. Cheng D, Gong Y, Zhou S, Wang J, Zheng N (2016) Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: CVPR

  10. Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. In: ICML

  11. Dikmen M, Akbas E, Huang TS, Ahuja N (2010) Pedestrian recognition with a learned metric. In: ACCV

  12. Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: CVPR

  13. Gheissari N, Sebastian TB, Hartley R (2006) Person reidentification using spatiotemporal appearance. In: CVPR

  14. Gong S, Cristani M, Yan S, Loy CC (2014) Person re-identification, vol 1. Springer

  15. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: PETS

  16. Guo X (2015) Robust subspace segmentation by simultaneously learning data representations and their affinity matrix. In: IJCAI

  17. Harandi M, Sanderson C, Shen C, Lovell BC (2013) Dictionary learning and sparse coding on grassmann manifolds: an extrinsic solution. In: ICCV

  18. Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: SCIA

  19. Hirzer M, Roth PM, Bischof H (2012) Person re-identification by efficient impostor-based metric learning. In: AVSS

  20. Jing XY, Zhu X, Wu F, You X, Liu Q, Yue D, Hu R, Xu B (2015) Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. In: CVPR

  21. Karanam S, Li Y, Radke RJ (2015) Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: ICCV

  22. Kodirov E, Xiang T, Gong S (2015) Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification. In: BMVC

  23. Koestinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: CVPR

  24. Li S, Yin H, Fang L (2012) Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Trans Biomed Eng 59(12):3450–3459

    Article  Google Scholar 

  25. Li Z, Chang S, Liang F, Huang TS, Cao L, Smith JR (2013) Learning locally-adaptive decision functions for person verification. In: CVPR

  26. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: deep filter pairing neural network for person re-identification. In: CVPR

  27. Li S, Shao M, Fu Y (2015) Cross-view projective dictionary learning for person re-identification. In: IJCAI

  28. Lisanti G, Masi I, Del Bimbo A (2014) Matching people across camera views using kernel canonical correlation analysis. In: ICDSC

  29. Lisanti G, Masi I, Bagdanov AD, Del Bimbo A (2015) Person re-identification by iterative re-weighted sparse ranking. IEEE Trans Pattern Anal Mach Intell 37(8):1629–1642

    Article  Google Scholar 

  30. Liu BD, Wang YX, Zhang YJ, Shen B (2013) Learning dictionary on manifolds for image classification. Pattern Recogn 46(7):1879–1890

    Article  Google Scholar 

  31. Liu X, Song M, Tao D, Zhou X, Chen C, Bu J (2014) Semi-supervised coupled dictionary learning for person re-identification. In: CVPR

  32. Ma B, Su Y, Jurie F (2012) Bicov: a novel image representation for person re-identification and face verification. In: BMVC

  33. Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: ICML

  34. Matsukawa T, Okabe T, Sato Y (2014) Person re-identification via discriminative accumulation of local features. In: ICPR

  35. Mignon A, Jurie F (2012) Pcca: a new approach for distance learning from sparse pairwise constraints. In: CVPR

  36. Nie F, Yuan J, Huang H (2014) Optimal mean robust principal component analysis. In: ICML

  37. Nie L, Zhang L, Wang M, Hong R, Farseev A, Chua TS (2017) Learning user attributes via mobile social multimedia analytics. ACM Trans Intell Syst Technol 8(3):36

    Article  Google Scholar 

  38. Pedagadi S, Orwell J, Velastin S, Boghossian B (2013) Local fisher discriminant analysis for pedestrian re-identification. In: CVPR

  39. Peng P, Xiang T, Wang Y, Pontil M, Gong S, Huang T, Tian Y (2016) Unsupervised cross-dataset transfer learning for person re-identification. In: CVPR

  40. Qi M, Han J, Jiang J, Liu H (2017) Deep feature representation and multiple metric ensembles for person re-identification in security surveillance system. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4649-2

  41. Skretting K, Engan K (2010) Recursive least squares dictionary learning algorithm. IEEE Trans Signal Process 58(4):2121–2130

    Article  MathSciNet  Google Scholar 

  42. Song X, Nie L, Zhang L, Liu M, Chua TS (2015) Interest inference via structure-constrained multi-source multi-task learning. In: IJCAI

  43. Varior RR, Wang G, Lu J, Liu T (2016) Learning invariant color features for person reidentification. IEEE Trans Image Process 25(7):3395–3410

    Article  MathSciNet  Google Scholar 

  44. Wang H, Gong S (2014) Xiang. Unsupervised learning of generative topic saliency for person re-identification, T.

    Google Scholar 

  45. Wang Z, Hu R, Liang C, Yu Y, Jiang J, Ye M, Chen J, Leng Q (2016) Zero-shot person re-identification via cross-view consistency. IEEE Trans Multimedia 18(2):260–272

    Article  Google Scholar 

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

    MATH  Google Scholar 

  47. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: CVPR

  48. Xiong F, Gou M, Camps O, Sznaier M (2014) Person re-identification using kernel-based metric learning methods. In: ECCV

  49. Xu Y, Guo J, Huang Z, Qiu W (2017) Sparse coding with cross-view invariant dictionaries for person re-identification. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4893-5

    Article  Google Scholar 

  50. Yang Y, Wen L, Lyu S, Li SZ (2017) Unsupervised learning of multi-level descriptors for person re-identification. In: AAAI

  51. Yu K, Zhang T, Gong Y (2009) Nonlinear learning using local coordinate coding. In: NIPS

  52. Zhang T (2009) Multi-stage convex relaxation for learning with sparse regularization. In: NIPS

  53. Zhang Q, Li B (2010) Discriminative k-svd for dictionary learning in face recognition. In: CVPR

  54. Zhao R, Ouyang W, Wang X (2013) Person re-identification by salience matching. In: ICCV

  55. Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: CVPR

  56. Zhao R, Ouyang W, Wang X (2014) Learning mid-level filters for person re-identification. In: CVPR

  57. Zheng WS, Gong S, Xiang T (2009) Associating groups of people. In: BMVC

  58. Zheng M, Bu J, Chen C, Wang C, Zhang L, Qiu G, Cai D (2011) Graph regularized sparse coding for image representation. IEEE Trans Image Process 20(5):1327–1336

    Article  MathSciNet  Google Scholar 

  59. Zheng WS, Gong S, Xiang T (2011) Person re-identification by probabilistic relative distance comparison. In: CVPR

  60. Zheng WS, Gong S, Xiang T (2013) Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668

    Article  Google Scholar 

Download references

Acknowledgments

This work was funded by the National Science Foundation of China (Nos. 61502377, 61532004), the National Key Research and Development Program of China (No. 2016YFB1000903); Ministry of Education Innovation Research Team (No. IRT_17R86); Project of China Knowledge Center for Engineering Science and Technology, and China Postdoctoral Science Foundation (No. 2015M582662).

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Authors and Affiliations

  1. SPKLSTN Lab, Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China

    Caixia Yan, Minnan Luo & Qinghua Zheng

  2. CAI, University of Technology Sydney, Sydney, Australia

    Wenhe Liu

Authors
  1. Caixia Yan
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  2. Minnan Luo
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  3. Wenhe Liu
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  4. Qinghua Zheng
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Correspondence to Minnan Luo.

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Yan, C., Luo, M., Liu, W. et al. Robust dictionary learning with graph regularization for unsupervised person re-identification. Multimed Tools Appl 77, 3553–3577 (2018). https://doi.org/10.1007/s11042-017-5202-z

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