Abstract
Person re-identification (re-ID) is factually a topic of pedestrian retrieval across camera scenes. However, it is challenging due to those factors such as complex equipment modeling, light change and occlusion. Much of the previous research is based on supervised methods that require labeling large amounts of data, which is expensive and time-consuming. The unsupervised re-ID methods without manual annotation usually need to construct pseudo-labels through clustering. However, the pseudo-labels noise may seriously affect the model’s performance. To deal with this issue, in this paper, we use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to assign pseudo-labels to samples and propose a model with the high-confidence samples’ labels (HCSL), which is a fully unsupervised learning method and does not use any labeled data. The model constructs high-confidence triplets through cyclic consistency and random image transformation, which reduces noise and makes the model finely distinguish the differences between classes. Experimental results show that the performance of our method on both Market-1501 and DukeMTMC-reID performs better than the latest unsupervised re-ID methods and even surpasses some unsupervised domain adaptation methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bottou, L.: Stochastic gradient descent tricks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 421–436. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_25
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: European Conference on Computer Vision (2018)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)
Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 994–1003 (2018)
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1422–1430. IEEE Computer Society, December 2015
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. AAAI Press, Palo Alto (1996)
Fan, H., Zheng, L., Yan, C., Yang, Y.: Unsupervised person re-identification: clustering and fine-tuning. ACM Trans. Multim. Comput. Commun. Appl. 83:1–83:18 (2018)
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2360–2367 (2010)
Fu, Y., et al.: Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6111–6120 (2019)
Gou, M., Fei, X., Camps, O., Sznaier, M.: Person re-identification using kernel-based metric learning methods. In: Computer Vision-ECCV 2014 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Komodakis, N., Gidaris, S.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (ICLR), Vancouver, Canada, April 2018
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105. NIPS 2012, Curran Associates Inc., Red Hook, NY, USA (2012)
Li, Y.J., Lin, C.S., Lin, Y.B., Wang, Y.: Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Liao, S., Yang, H., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Lin, Y., Xie, L., Wu, Y., Yan, C., Tian, Q.: Unsupervised person re-identification via softened similarity learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8738–8745 (2019)
Lin, Y., Xie, L., Wu, Y., Yan, C., Tian, Q.: Unsupervised person re-identification via softened similarity learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3387–3396 (2020)
Lisanti, G., Masi, I., Bagdanov, A.D., Bimbo, A.D.: Person re-identification by iterative re-weighted sparse ranking. IEEE Trans. Patt. Anal. Mach. Intell. 37, 1629–1642 (2015)
Liu, J., Zha, Z.J., Chen, D., Hong, R., Wang, M.: Adaptive transfer network for cross-domain person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Liu, L., et al.: On the variance of the adaptive learning rate and beyond. In: International Conference on Learning Representations (2020)
Long, M., Wang, J.: Learning transferable features with deep adaptation networks. JMLR.org (2015)
Lu, Y., et al.: Cross-modality person re-identification with shared-specific feature transfer. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Martinel, N., Micheloni, C., Foresti, G.L.: Saliency weighted features for person re-identification. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8927, pp. 191–208. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16199-0_14
Niu, C., Zhang, J., Wang, G., Liang, J.: GATCluster: self-supervised gaussian-attention network for image clustering. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 735–751. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_44
Peng, P., et al.: Unsupervised cross-dataset transfer learning for person re-identification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1306–1315 (2016)
Qi, L., Wang, L., Huo, J., Zhou, L., Shi, Y., Gao, Y.: A novel unsupervised camera-aware domain adaptation framework for person re-identification. In: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, 27 October–2 November 2019, Seoul, Korea (South), pp. 8079–8088. IEEE (2019)
Roth, P.M., Hirzer, M., Kstinger, M., Beleznai, C., Bischof, H.: Mahalanobis distance learning for person re-identification. Person Re-Identification (2014)
Rui, Z., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: Proceedings of the 2013 IEEE International Conference on Computer Vision (2013)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823 (2015)
Tang, H., Zhao, Y., Lu, H.: Unsupervised person re-identification with iterative self-supervised domain adaptation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019)
Tay, C.P., Roy, S., Yap, K.H.: AANet: attribute attention network for person re-identifications. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Wang, D., Zhang, S.: Unsupervised person re-identification via multi-label classification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10978–10987 (2020)
Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2275–2284 (2018)
Wei, L., Zhang, S., Wen, G., Qi, T.: Person transfer GAN to bridge domain gap for person re-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Wu, A., Zheng, W.S., Lai, J.H.: Unsupervised person re-identification by camera-aware similarity consistency learning. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6921–6930 (2019)
Wu, C.Y., Manmatha, R., Smola, A.J., Krhenbühl, P.: Sampling matters in deep embedding learning. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Wu, J., Xiong, H., Chen, J.: Adapting the right measures for k-means clustering. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 877–886. Association for Computing Machinery (2009)
Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.: Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Yu, H.X., Zheng, W.S.: Weakly supervised discriminative feature learning with state information for person identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Zhan, X., Xie, J., Liu, Z., Ong, Y.S., Loy, C.C.: Online deep clustering for unsupervised representation learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Zhao, Y., Shen, X., Jin, Z., Lu, H., Hua, X.: Attribute-driven feature disentangling and temporal aggregation for video person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4908–4917, June 2019
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1116–1124 (2015)
Zheng, W.S., Gong, S., Xiang, T.: Reidentification by relative distance comparison. IEEE Trans. Softw. Eng. 35, 653–668 (2012)
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Zhong, Z., Zheng, L., Li, S., Yang, Y.: Generalizing a person retrieval model hetero- and homogeneously. In: Proceedings of the European Conference on Computer Vision (ECCV), September 2018
Zhu, Z., Jiang, X., Zheng, F., Guo, X., Zheng, W.: Viewpoint-aware loss with angular regularization for person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 13114–13121 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, L., Zhao, Q., Wang, S., Lu, J., Zhao, Y. (2022). High-Confidence Sample Labelling for Unsupervised Person Re-identification. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_5
Download citation
DOI: https://doi.org/10.1007/978-981-16-9247-5_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9246-8
Online ISBN: 978-981-16-9247-5
eBook Packages: Computer ScienceComputer Science (R0)