Advertisement

Invariance Matters: Person Re-identification by Local Color Transfer

  • Ying Niu
  • Chunmiao Yuan
  • Kunliang Liu
  • Yukuan Sun
  • Jiayu Liang
  • Guanghao Jin
  • Jianming WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

Person re-identification is a complex image retrieval problem. The color of the image is distorted due to changes in illumination, etc., which makes pedestrian recognition more challenging. In this paper, we take the conditional image, the reference image and its corresponding clothing segmentation image as input, and then restore the true color of the person through color conversion. In addition, we calculate the similarity between the conditional image and the image dataset by the chromatic aberration similarity and the clothing segmentation invariance. We evaluated the proposed method on a public dataset. A large number of experimental results show that the method is effective.

Keywords

Color transfer Similarity Segmentation Re-identification 

References

  1. 1.
    Bhuiyan, A., Perina, A., Murino, V.: Exploiting multiple detections to learn robust brightness transfer functions in re-identification systems. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2329–2333. IEEE (2015)Google Scholar
  2. 2.
    Champandard, A.J.: Semantic style transfer and turning two-bit doodles into fine artworks. arXiv preprint arXiv:1603.01768 (2016)
  3. 3.
    Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3291–3300 (2018)Google Scholar
  4. 4.
    Chen, D., Yuan, Z., Hua, G., Zheng, N., Wang, J.: Similarity learning on an explicit polynomial kernel feature map for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1565–1573 (2015)Google Scholar
  5. 5.
    Chen, J., Zhang, Z., Wang, Y.: Relevance metric learning for person re-identification by exploiting global similarities. In: 2014 22nd International Conference on Pattern Recognition, pp. 1657–1662. IEEE (2014)Google Scholar
  6. 6.
    Chen, Y.S., Wang, Y.C., Kao, M.H., Chuang, Y.Y.: Deep photo enhancer: unpaired learning for image enhancement from photographs with GANS. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6306–6314 (2018)Google Scholar
  7. 7.
    Eilertsen, G., Kronander, J., Denes, G., Mantiuk, R.K., Unger, J.: HDR image reconstruction from a single exposure using deep CNNs. ACM Trans. Graph. (TOG) 36(6), 178 (2017)CrossRefGoogle Scholar
  8. 8.
    Gardner, J.R., et al.: Deep manifold traversal: Changing labels with convolutional features. arXiv preprint arXiv:1511.06421 (2015)
  9. 9.
    Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)Google Scholar
  10. 10.
    Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 498–505. IEEE (2009)Google Scholar
  11. 11.
    Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288–2295. IEEE (2012)Google Scholar
  12. 12.
    Laffont, P.Y., Ren, Z., Tao, X., Qian, C., Hays, J.: Transient attributes for high-level understanding and editing of outdoor scenes. ACM Trans. Graph. (TOG) 33(4), 149 (2014)CrossRefGoogle Scholar
  13. 13.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2007)CrossRefGoogle Scholar
  14. 14.
    Li, W., Wang, X.: Locally aligned feature transforms across views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3594–3601 (2013)Google Scholar
  15. 15.
    Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R.: Learning locally-adaptive decision functions for person verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3610–3617 (2013)Google Scholar
  16. 16.
    Liang, X., Gong, K., Shen, X., Lin, L.: Look into person: joint body parsing & pose estimation network and a new benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 871–885 (2019)CrossRefGoogle Scholar
  17. 17.
    Liang, X., et al.: Deep human parsing with active template regression. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2402–2414 (2015)CrossRefGoogle Scholar
  18. 18.
    Liang, X., Shen, X., Xiang, D., Feng, J., Lin, L., Yan, S.: Semantic object parsing with local-global long short-term memory. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3185–3193 (2016)Google Scholar
  19. 19.
    Liang, X., et al.: Human parsing with contextualized convolutional neural network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1386–1394 (2015)Google Scholar
  20. 20.
    Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4990–4998 (2017)Google Scholar
  21. 21.
    Paisitkriangkrai, S., Shen, C., Van Den Hengel, A.: 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 (2015)Google Scholar
  22. 22.
    Shih, Y., Paris, S., Durand, F., Freeman, W.T.: Data-driven hallucination of different times of day from a single outdoor photo. ACM Trans. Graph. (TOG) 32(6), 200 (2013)CrossRefGoogle Scholar
  23. 23.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  24. 24.
    Song, C., Huang, Y., Ouyang, W., Wang, L.: Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1179–1188 (2018)Google Scholar
  25. 25.
    Sun, X., Zheng, L.: Dissecting person re-identification from the viewpoint of viewpoint. arXiv preprint arXiv:1812.02162 (2018)
  26. 26.
    Sun, Y., et al.: Perceive where to focus: Learning visibility-aware part-level features for partial person re-identification. arXiv preprint arXiv:1904.00537 (2019)
  27. 27.
    Wang, Y., Hu, R., Liang, C., Zhang, C., Leng, Q.: Camera compensation using a feature projection matrix for person reidentification. IEEE Trans. Circuits Syst. Video Technol. 24(8), 1350–1361 (2014)CrossRefGoogle Scholar
  28. 28.
    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_1CrossRefGoogle Scholar
  29. 29.
    Zeng, Z., Wang, Z., Wang, Z., Chuang, Y.Y., Satoh, S.: Illumination-adaptive person re-identification. arXiv preprint arXiv:1905.04525 (2019)
  30. 30.
    Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2528–2535 (2013)Google Scholar
  31. 31.
    Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984 (2016)
  32. 32.
    Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q.: Person re-identification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1367–1376 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ying Niu
    • 1
  • Chunmiao Yuan
    • 1
  • Kunliang Liu
    • 1
  • Yukuan Sun
    • 2
  • Jiayu Liang
    • 1
  • Guanghao Jin
    • 1
    • 3
  • Jianming Wang
    • 2
    • 4
    Email author
  1. 1.School of Computer Science and TechnologyTianjin Polytechnic UniversityTianjinChina
  2. 2.School of Electronics and Information EngineeringTianjin Polytechnic UniversityTianjinChina
  3. 3.Tianjin International Joint Research and Development Center of Autonomous Intelligence Technology and SystemsTianjin Polytechnic UniversityTianjinChina
  4. 4.Tianjin Key Laboratory of Autonomous Intelligence Technology and SystemsTianjin Polytechnic UniversityTianjinChina

Personalised recommendations