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Early Active Learning with Pairwise Constraint for Person Re-identification

  • Wenhe Liu
  • Xiaojun Chang
  • Ling Chen
  • Yi Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10534)

Abstract

Research on person re-identification (re-id) has attached much attention in the machine learning field in recent years. With sufficient labeled training data, supervised re-id algorithm can obtain promising performance. However, producing labeled data for training supervised re-id models is an extremely challenging and time-consuming task because it requires every pair of images across no-overlapping camera views to be labeled. Moreover, in the early stage of experiments, when labor resources are limited, only a small number of data can be labeled. Thus, it is essential to design an effective algorithm to select the most representative samples. This is referred as early active learning or early stage experimental design problem. The pairwise relationship plays a vital role in the re-id problem, but most of the existing early active learning algorithms fail to consider this relationship. To overcome this limitation, we propose a novel and efficient early active learning algorithm with a pairwise constraint for person re-identification in this paper. By introducing the pairwise constraint, the closeness of similar representations of instances is enforced in active learning. This benefits the performance of active learning for re-id. Extensive experimental results on four benchmark datasets confirm the superiority of the proposed algorithm.

Keywords

Early active learning Person re-identification 

Notes

Acknowledgements

This work was partially supported by the Data to Decisions Cooperative Research Centre www.d2dcrc.com.au and partially supported by the National Science Foundation under Grant No. IIS-1638429.

References

  1. 1.
    Balcan, M.-F., Broder, A., Zhang, T.: Margin based active learning. In: Bshouty, N.H., Gentile, C. (eds.) COLT 2007. LNCS (LNAI), vol. 4539, pp. 35–50. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72927-3_5 CrossRefGoogle Scholar
  2. 2.
    Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: Proceedings of the British Machine Vision Conference (BMVC) (2011)Google Scholar
  3. 3.
    Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Mach. Learn. 28(2), 133–168 (1997)CrossRefzbMATHGoogle Scholar
  4. 4.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3 (2007)Google Scholar
  5. 5.
    Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21227-7_9 CrossRefGoogle Scholar
  6. 6.
    Huang, S.J., Jin, R., Zhou, Z.H.: Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems (NIPS), pp. 892–900 (2010)Google Scholar
  7. 7.
    Karanam, S., Li, Y., Radke, R.J.: Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4516–4524 (2015)Google Scholar
  8. 8.
    Karanam, S., Li, Y., Radke, R.J.: Sparse re-id: block sparsity for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 33–40 (2015)Google Scholar
  9. 9.
    Kodirov, E., Xiang, T., Fu, Z., Gong, S.: Person re-identification by unsupervised \(\ell _1\) graph learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 178–195. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_11 CrossRefGoogle Scholar
  10. 10.
    Kodirov, E., Xiang, T., Gong, S.: Dictionary learning with iterative Laplacian regularisation for unsupervised person re-identification. In: Proceedings of the British Machine Vision Conference (BMVC), vol. 3, p. 8 (2015)Google Scholar
  11. 11.
    Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Proceedings of the Eleventh International Conference on Machine Learning, pp. 148–156 (1994)Google Scholar
  12. 12.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: 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 (2015)Google Scholar
  13. 13.
    Lisanti, G., Masi, I., Bagdanov, A.D., Del Bimbo, A.: Person re-identification by iterative re-weighted sparse ranking. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1629–1642 (2015)CrossRefGoogle Scholar
  14. 14.
    Lisanti, G., Masi, I., Del Bimbo, A.: Matching people across camera views using kernel canonical correlation analysis. In: Proceedings of the International Conference on Distributed Smart Cameras, p. 10. ACM (2014)Google Scholar
  15. 15.
    Ma, A.J., Li, P.: Semi-supervised ranking for re-identification with few labeled image pairs. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 598–613. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16817-3_39 Google Scholar
  16. 16.
    Nguyen, H.T., Smeulders, A.: Active learning using pre-clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning. ACM (2004)Google Scholar
  17. 17.
    Nie, F., Huang, H., Cai, X., Ding, C.H.: Efficient and robust feature selection via joint \(\ell _{2, 1}\)-norms minimization. In: Advances in Neural Information Processing Systems (NIPS), pp. 1813–1821 (2010)Google Scholar
  18. 18.
    Nie, F., Wang, H., Huang, H., Ding, C.H.: Early active learning via robust representation and structured sparsity. In: International Joint Conference on Artificial Intelligence (IJCAI) (2013)Google Scholar
  19. 19.
    Nie, F., Xu, D., Li, X.: Initialization independent clustering with actively self-training method. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(1), 17–27 (2012)CrossRefGoogle Scholar
  20. 20.
    Peng, P., Xiang, T., Wang, Y., Pontil, M., Gong, S., Huang, T., Tian, Y.: Unsupervised cross-dataset transfer learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1306–1315 (2016)Google Scholar
  21. 21.
    Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 287–294. ACM (1992)Google Scholar
  22. 22.
    Twomey, N., Diethe, T., Flach, P.: Bayesian active learning with evidence-based instance selection. In: Workshop on Learning over Multiple Contexts, European Conference on Machine Learning (ECML 2015) (2015)Google Scholar
  23. 23.
    Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1249–1258 (2016)Google Scholar
  24. 24.
    Xiong, F., Gou, M., Camps, O., Sznaier, M.: Person re-identification using kernel-based metric learning methods. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 1–16. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10584-0_1 Google Scholar
  25. 25.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40-51 (2007)Google Scholar
  26. 26.
    Yang, Y., Shen, H.T., Ma, Z., Huang, Z., Zhou, X.: \(\ell _{2, 1}\)-norm regularized discriminative feature selection for unsupervised learning. In: International Joint Conference on Artificial Intelligence (IJCAI) (2011)Google Scholar
  27. 27.
    Yu, K., Bi, J., Tresp, V.: Active learning via transductive experimental design. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 1081–1088. ACM (2006)Google Scholar
  28. 28.
    Zhang, L., Xiang, T., Gong, S.: Learning a discriminative null space for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1239–1248 (2016)Google Scholar
  29. 29.
    Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984 (2016)
  30. 30.
    Zheng, M., Bu, J., Chen, C., Wang, C., Zhang, L., Qiu, G., Cai, D.: Graph regularized sparse coding for image representation. IEEE Trans. Image Process. 20(5), 1327–1336 (2011)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CAIUniversity of Technology SydneySydneyAustralia
  2. 2.LTICarnegie Mellon UniversityPittsburghUSA

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