Skip to main content
SpringerLink
Log in

Deep Supervised Hashing with Triplet Labels

  • Conference paper
  • First Online:
Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10111))

Included in the following conference series:

Abstract

Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in sub-optimal hash codes. Recently, deep hashing methods have been proposed to simultaneously learn image features and hash codes using deep neural networks and have shown superior performance over traditional hashing methods. Most deep hashing methods are given supervised information in the form of pairwise labels or triplet labels. The current state-of-the-art deep hashing method DPSH [1], which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities. Inspired by DPSH [1], we propose a triplet label based deep hashing method which aims to maximize the likelihood of the given triplet labels. Experimental results show that our method outperforms all the baselines on CIFAR-10 and NUS-WIDE datasets, including the state-of-the-art method DPSH [1] and all the previous triplet label based deep hashing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We communicated with the authors of DPSH. The main difference between our experiments and their experiments is the step size and decay factor for learning rate change. They say that with our parameters, they can also get better results than what is reported in their paper [1].

References

  1. Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels. arXiv preprint arXiv:1511.03855 (2015)

  2. Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 817–824. IEEE (2011)

    Google Scholar 

  3. Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3270–3278 (2015)

    Google Scholar 

  4. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42, 145–175 (2001)

    Article  MATH  Google Scholar 

  5. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)

    Article  Google Scholar 

  6. Zhao, F., Huang, Y., Wang, L., Tan, T.: Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1556–1564 (2015)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  8. Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 47th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2006, pp. 459–468. IEEE (2006)

    Google Scholar 

  9. Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. In: Advances in Neural Information Processing Systems, pp. 1509–1517 (2009)

    Google Scholar 

  10. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)

    Google Scholar 

  11. Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems, pp. 1042–1050 (2009)

    Google Scholar 

  12. Kong, W., Li, W.J.: Isotropic hashing. In: Advances in Neural Information Processing Systems, pp. 1646–1654 (2012)

    Google Scholar 

  13. Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 1–8 (2011)

    Google Scholar 

  14. Liu, W., Mu, C., Kumar, S., Chang, S.F.: Discrete graph hashing. In: Advances in Neural Information Processing Systems, pp. 3419–3427 (2014)

    Google Scholar 

  15. Jiang, Q.Y., Li, W.J.: Scalable graph hashing with feature transformation. In: Proceedings of the International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  16. Salakhutdinov, R., Hinton, G.: Semantic hashing. Int. J. Approximate Reasoning 50, 969–978 (2009)

    Article  Google Scholar 

  17. Erin Liong, V., Lu, J., Wang, G., Moulin, P., Zhou, J.: Deep hashing for compact binary codes learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2475–2483 (2015)

    Google Scholar 

  18. Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 27–35 (2015)

    Google Scholar 

  19. Norouzi, M., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: ICML, vol. 1, p. 2 (2011)

    Google Scholar 

  20. Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2074–2081. IEEE (2012)

    Google Scholar 

  21. Zhang, P., Zhang, W., Li, W.J., Guo, M.: Supervised hashing with latent factor models. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 173–182. ACM (2014)

    Google Scholar 

  22. Lin, G., Shen, C., Shi, Q., Hengel, A., Suter, D.: Fast supervised hashing with decision trees for high-dimensional data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1963–1970 (2014)

    Google Scholar 

  23. Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: AAAI, vol. 1, p. 2 (2014)

    Google Scholar 

  24. Wang, J., Liu, W., Sun, A., Jiang, Y.G.: Learning hash codes with listwise supervision. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3032–3039 (2013)

    Google Scholar 

  25. Li, X., Lin, G., Shen, C., Van den Hengel, A., Dick, A.: Learning hash functions using column generation. In: Proceedings of The 30th International Conference on Machine Learning, pp. 142–150(2013)

    Google Scholar 

  26. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014)

  27. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  28. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)

  29. Kang, W.C., Li, W.J., Zhou, Z.H.: Column sampling based discrete supervised hashing (2016)

    Google Scholar 

  30. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)

    Google Scholar 

  31. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceedings of the ACM International Conference on Image and Video Retrieval, p. 48. ACM (2009)

    Google Scholar 

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

    Google Scholar 

  33. Shen, F., Shen, C., Liu, W., Tao Shen, H.: Supervised discrete hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 37–45 (2015)

    Google Scholar 

Download references

Acknowledgement

This work was sponsored by DARPA under agreement number FA8750-14-2-0244. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government.

Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA

    Xiaofang Wang, Yi Shi & Kris M. Kitani

Authors
  1. Xiaofang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kris M. Kitani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaofang Wang .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, X., Shi, Y., Kitani, K.M. (2017). Deep Supervised Hashing with Triplet Labels. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54181-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54180-8

  • Online ISBN: 978-3-319-54181-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation