Skip to main content
SpringerLink
Log in

MEIAH: Mixing explicit and implicit formulation of attributes in binary representation for person re-identification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Person Re-identification (ReID) is an important yet challenging task in computer vision. It is far from solved due to the diverse background clutters, variations on viewpoints and body poses. On top of it, effective fast re-identification with binary representation is far more challenging. In this context, how to extract discriminative and robust binary features for identifying people in a large gallery is the core problem. It is observed that the pedestrian attribute labels can be good auxiliary information for learning better features for ReID task, but in most of the application scenarios we do not have the labeled training set with both pedestrian ID and attributes. In this paper, we first introduce a multi-task training method with data from target domain and auxiliary domain with different label types that is able to Mix Explicit and Implicit Attributes for Hashing (MEIAH). MEIAH is a novel end-to-end multi-task model to learn a mixed binary representation with explicit and implicit formulation of attributes for better ReID performance. Our architecture effectively unifies and takes full advantage of information from different domains. We evaluate the proposed method in four different bit lengths on two public benchmark datasets, including CUHK03 and Market-1501. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results.

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

Similar content being viewed by others

References

  1. Cao Y, Long M, Wang J, Zhu H, Wen Q (2016) Deep quantization network for efficient image retrieval. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, pp 3457–3463

  2. Chen H, Gallagher AC, Girod B (2012) Describing clothing by semantic attributes. In: Computer vision - ECCV 2012 - 12th european conference on computer vision. Proceedings, Part III, Florence

  3. Chen J, Wang Y, Qin J, Liu L, Shao L (2017) Fast person re-identification via cross-camera semantic binary transformation. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017, Honolulu, pp 5330–5339

  4. Chen W, Chen X, Zhang J, Huang K (2017) Beyond triplet loss: a deep quadruplet network for person re-identification. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017, Honolulu, pp 1320–1329

  5. Chen W, Chen X, Zhang J, Huang K (2017) A multi-task deep network for person re-identification. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, pp 3988–3994

  6. Chen Y, Zhu X, Gong S (2017) Person re-identification by deep learning multi-scale representations. In: 2017 IEEE International conference on computer vision workshops, ICCV workshops 2017, Venice, pp 2590–2600

  7. Cheng Z, Shen J (2016) On very large scale test collection for landmark image search benchmarking. Signal Process 124:13–26

    Article  Google Scholar 

  8. Cheng Z, Ding Y, He X, Zhu L, Song X, Kankanhalli M (2018) Aˆ 3ncf: an adaptive aspect attention model for rating prediction. In: IJCAI, pp 3748–3754

  9. Cheng Z, Ding Y, Zhu L, Kankanhalli M (2018) Aspect-aware latent factor model: rating prediction with ratings and reviews. arXiv:1802.07938

  10. Chung D, Tahboub K, Delp EJ (2017) A two stream siamese convolutional neural network for person re-identification. In: IEEE International conference on computer vision, ICCV 2017, Venice, pp 1992–2000

  11. Deng Y, Luo P, Loy CC, Tang X (2014) Pedestrian attribute recognition at far distance. In: Proceedings of the ACM International Conference on Multimedia, MM ’14, Orlando

  12. Deng Y, Luo P, Loy CC, Tang X (2015) Learning to recognize pedestrian attribute coRR

  13. Fabbri M, Calderara S, Cucchiara R (2017) Generative adversarial models for people attribute recognition in surveillance. In: 14Th IEEE international conference on advanced video and signal based surveillance, AVSS 2017, Lecce

  14. Gionis A, Indyk P, Motwani R (1999) Similarity search in high dimensions via hashing. In: VLDB

  15. Gong Y, Lazebnik S, Gordo A, Perronnin F (2013) Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval PAMI

  16. He K, Wen F, Sun J (2013) K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: CVPR

  17. Heo J, Lee Y, He J, Chang S, Yoon S (2012) Spherical hashing. In: 2012 IEEE Conference on computer vision and pattern recognition, providence, RI, pp 2957–2964

  18. Jaha ES, Nixon MS (2014) Soft biometrics for subject identification using clothing attributes. In: IEEE International joint conference on biometrics, clearwater, IJCB 2014, FL, USA

  19. Jin Z, Li C, Lin Y, Cai D (2014) Density sensitive hashing. IEEE Trans Cybern 44(8):1362–1371

    Article  Google Scholar 

  20. Kong W, Li W (2012) Isotropic hashing. In: NIPS

  21. Kulis B, Darrell T (2009) Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, pp 1042–1050

  22. Lai H, Pan Y, Liu Y, Yan S (2015) Simultaneous feature learning and hash coding with deep neural networks. In: CVPR

  23. Layne R, Hospedales TM, Gong S (2012) Person re-identification by attributes. In: British machine vision conference, BMVC 2012, Surrey, UK

  24. Layne R, Hospedales TM, Gong S (2014) Re-id: Hunting attributes in the wild. In: British machine vision conference, BMVC 2014, Nottingham, UK

  25. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: Deep filter pairing neural network for person re-identification. In: 2014 IEEE Conference on computer vision and pattern recognition, CVPR 2014, Columbus, OH, pp 152–159

  26. Li D, Chen X, Huang K (2015) Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. In: 3Rd IAPR asian conference on pattern recognition, ACPR 2015, Kuala Lumpur

  27. Li D, Zhang Z, Chen X, Ling H, Huang K (2016) A richly annotated dataset for pedestrian attribute recognition coRR

  28. Li D, Chen X, Zhang Z, Huang K (2017) Learning deep context-aware features over body and latent parts for person re-identification. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017, Honolulu, HI, pp 7398–7407

  29. Li Y, Lin G, Zhuang B, Liu L, Shen C, van den Hengel A (2017) Sequential person recognition in photo albums with a recurrent network. In: CVPR

  30. Liao S, Hu Y, Zhu X, Li S (2015) Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on computer vision and pattern recognition, CVPR 2015, Boston, MA, pp 2197–2206

  31. Lin K, Yang H, Hsiao J, Chen C (2015) Deep learning of binary hash codes for fast image retrieval. In: 2015 IEEE Conference on computer vision and pattern recognition workshops, CVPR workshops, Boston, MA, pp 27–35

  32. Lin Y, Zheng L, Zheng Z, Wu Y, Yang Y (2017) Improving person re-identification by attribute and identity learning. CoRR arXiv:1703.07220

  33. Liu C, Gong S, Loy CC, Lin X (2012) Person re-identification: what features are important?. In: Computer vision - ECCV 2012. Workshops and demonstrations - florence. Proceedings, Part I, Italy

  34. Liu W, Wang J, Ji R, Jiang Y, Chang S (2012) Supervised hashing with kernels. In: CVPR

  35. Liu X, Song M, Zhao Q, Tao D, Chen C, Bu J (2012) Attribute-restricted latent topic model for person re-identification. Pattern Recogn 45(12):4204–4213

    Article  Google Scholar 

  36. Liu W, Mu C, Kumar S, Chang S (2014) Discrete graph hashing. In: NIPS

  37. Liu S, Lu H (2016) Accurate deep representation quantization with gradient snapping layer for similarity search coRR

  38. Liu F, Xiang T, Hospedales TM, Yang W, Sun C (2017) Semantic regularisation for recurrent image annotation. In: CVPR

  39. Liu X, Zhao H, Tian M, Sheng L, Shao J, Yi S, Yan J, Wang X (2017) Hydraplus-net: Attentive deep features for pedestrian analysis. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy

  40. Liu Z, Qin J, Li A, Wang Y, Gool LV (2018) Adversarial binary coding for efficient person re-identification. CoRR arXiv:1803.10914

  41. Norouzi M, Fleet DJ (2011) Minimal loss hashing for compact binary codes. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, pp 353–360

  42. Peng P, Tian Y, Xiang T, Wang Y, Huang T (2016) Joint learning of semantic and latent attributes. In: Computer vision - ECCV 2016 - 14th european conference. Proceedings, Part IV, Amsterdam

  43. Qian X, Fu Y, Jiang Y, Xiang T, Xue X (2017) Multi-scale deep learning architectures for person re-identification. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy, pp 5409–5418

  44. Shen F, Shen C, Liu W, Shen HT (2015) Supervised discrete hashing. In: CVPR

  45. Shen F, Shen C, Shi Q, van den Hengel A, Tang Z, Shen HT (2015) Hashing on nonlinear manifolds TIP

  46. Shi Z, Hospedales TM, Xiang T (2015) Transferring a semantic representation for person re-identification and search. In: CVPR

  47. Song J, Zhang H, Li X, Gao L, Wang M, Hong R (2018) Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans Image Process 27 (7):3210–3221

    Article  MathSciNet  MATH  Google Scholar 

  48. Su C, Zhang S, Xing J, Gao W, Tian Q (2016) Deep attributes driven multi-camera person re-identification. In: Computer vision - ECCV 2016 - 14th european conference. Proceedings, Part II, Amsterdam, pp 475–491

  49. Su C, Zhang S, Yang F, Zhang G, Tian Q, Gao W, Davis LS (2017) Attributes driven tracklet-to-tracklet person re-identification using latent prototypes space mapping. Pattern Recogn 66:4–15

    Article  Google Scholar 

  50. Su C, Yang F, Zhang S, Tian Q, Davis LS, Gao W (2018) Multi-task learning with low rank attribute embedding for multi-camera person re-identification. IEEE Trans. Pattern Anal Mach Intell 40(5):1167–1181

    Article  Google Scholar 

  51. Sudowe P, Spitzer H, Leibe B (2015) Person attribute recognition with a jointly-trained holistic CNN model. In: 2015 IEEE International conference on computer vision workshop, ICCV workshops 2015, Santiago, Chile

  52. Sun Y, Zheng L, Deng W, Wang S (2017) Svdnet for pedestrian retrieval. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy, pp 3820–3828

  53. Varior RR, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: Computer vision - ECCV 2016 - 14th european conference, Amsterdam, The Netherlands, Proceedings, Part VIII, pp 791–808

  54. Wang J, Kumar S, Chang S (2010) Sequential projection learning for hashing with compact codes. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel, pp 1127–1134

  55. Wang J, Liu W, Kumar S, Chang S (2016) Learning to hash for indexing big data - A survey. Proceedings of the IEEE

  56. Wang J, Zhu X, Gong S, Li W (2017) Attribute recognition by joint recurrent learning of context and correlation. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy

  57. Weiss Y, Torralba A, Fergus R (2008) Spectral hashing. In: NIPS

  58. Wu L, Shen C, van den Hengel A (2016) Personnet: Person re-identification with deep convolutional neural networks coRR

  59. Wu L, Wang Y, Ge Z, Hu Q, Li X (2018) Structured deep hashing with convolutional neural networks for fast person re-identification. Comput Vis Image Underst 167:63–73

    Article  Google Scholar 

  60. Xia R, Pan Y, Lai H, Liu C, Yan S (2014) Supervised hashing for image retrieval via image representation learning. In: AAAI

  61. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: 2016 IEEE Conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, pp 1249–1258

  62. Xiao T, Li S, Wang B, Lin L, Wang X (2017) Joint detection and identification feature learning for person search. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017, Honolulu, HI, pp 3376–3385

  63. Xie L, Shen J, Han J, Zhu L, Shao L (2017) Dynamic multi-view hashing for online image retrieval IJCAI

  64. Xu B, Bu J, Lin Y, Chen C, He X, Cai D (2013) Harmonious hashing. In: IJCAI

  65. Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. In: 2016 IEEE Conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, pp 1239–1248

  66. Zhang P, Zhang W, Li W, Guo M (2014) Supervised hashing with latent factor models. In: SIGIR

  67. Zhang R, Lin L, Zhang R, Zuo W, Zhang L (2015) Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans Image Process 24(12):4766–4779

    Article  MathSciNet  MATH  Google Scholar 

  68. Zhao F, Huang Y, Wang L, Tan T (2015) Deep semantic ranking based hashing for multi-label image retrieval. In: IEEE Conference on computer vision and pattern recognition, CVPR 2015, Boston, MA, pp 1556–1564

  69. Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S, Wang X, Tang X (2017) Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017, Honolulu, HI, pp 907–915

  70. Zhao R, Ouyang W, Wang X (2017) Person re-identification by saliency learning. IEEE Trans Pattern Anal Mach Intell 39(2):356–370

    Article  Google Scholar 

  71. Zhao X, Ding G, Guo Y, Han J, Gao Y (2017) TUCH: turning cross-view hashing into single-view hashing via generative adversarial nets. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, pp 3511–3517

  72. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark. In: 2015 IEEE International conference on computer vision, ICCV 2015, Santiago, Chile, pp 1116–1124

  73. Zheng F, Shao L (2016) Learning cross-view binary identities for fast person re-identification. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, pp 2399–2406

  74. Zheng L, Bie Z, Sun Y, Wang J, Su C, Wang S, Tian Q (2016) MARS: A video benchmark for large-scale person re-identification. In: Computer vision - ECCV 2016 - 14th european conference, Amsterdam, The Netherlands, Proceedings, Part VI, pp 868–884

  75. Zhou Z, Huang Y, Wang W, Wang L, Tan T (2017) See the forest for the trees: Joint spatial and temporal recurrent neural networks for video-based person re-identification. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017, Honolulu, HI, pp 6776–6785

  76. Zhu J, Liao S, Yi D, Lei Z, Li S (2015) Multi-label CNN based pedestrian attribute learning for soft biometrics. In: International conference on biometrics, ICB 2015, Phuket, Thailand

  77. Zhu H, Long M, Wang J, Cao Y (2016) Deep hashing network for efficient similarity retrieval. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, pp 2415–2421

  78. Zhu F, Kong X, Zheng L, Fu H, Tian Q (2017) Part-based deep hashing for large-scale person re-identification. IEEE Trans Image Process 26(10):4806–4817

    Article  MathSciNet  Google Scholar 

  79. Zhu L, Huang Z, Chang X, Song J, Shen HT (2017) Exploring consistent preferences: Discrete hashing with pair-exemplar for scalable landmark search. In: Proceedings of the 2017 ACM on Multimedia Conference, MM ’17. ACM, New York, pp 726–734. https://doi.org/10.1145/3123266.3123301

  80. Zhu L, Huang Z, Liu X, He X, Sun J, Zhou X (2017) Discrete multimodal hashing with canonical views for robust mobile landmark search. IEEE Trans Multimed 19(9):2066–2079

    Article  Google Scholar 

  81. Zhu L, Huang Z, Li Z, Xie L, Shen HT (2018) Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval. IEEE Transactions on Neural Networks and Learning Systems 29(11):5264–5276

Download references

Author information

Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, 100084, China

    Liufang Sang, Xin Zhao & Guiguang Ding

Authors
  1. Liufang Sang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guiguang Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guiguang Ding.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sang, L., Zhao, X. & Ding, G. MEIAH: Mixing explicit and implicit formulation of attributes in binary representation for person re-identification. Multimed Tools Appl 78, 27533–27551 (2019). https://doi.org/10.1007/s11042-019-07743-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-07743-6

Keywords

Search

Navigation