Supervised Representation Hash Codes Learning

  • Huei-Fang YangEmail author
  • Cheng-Hao Tu
  • Chu-Song Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1013)


Learning-based hashing has been widely employed for large-scale similarity retrieval due to its efficient computation and compressed storage. In this paper, we propose ResHash, a deep representation hash code learning approach to learning compact and discriminative binary codes. In ResHash, we assume that each semantic label has its own representation codeword and these codewords guide hash coding. The codewords are attractors that attract semantically similar images and are also repulsors that repel semantically dissimilar ones. Furthermore, ResHash jointly learns compact binary codes and discover representation codewords from data by a simple margin ranking loss, making it easily realizable and avoiding the need to hand-craft the codewords beforehand. Experimental results on standard benchmark datasets show the effectiveness of ResHash.


Image retrieval Binary codes Deep learning 


  1. 1.
    Bojanowski, P., Joulin, A., Lopez-Paz, D., Szlam, A.: Optimizing the latent space of generative networks. CoRR abs/1707.05776 (2017)Google Scholar
  2. 2.
    Cao, Y., Long, M., Wang, J., Liu, S.: Deep visual-semantic quantization for efficient image retrieval. In: CVPR (2017)Google Scholar
  3. 3.
    Cao, Z., Long, M., Wang, J., Yu, P.S.: HashNet: deep learning to hash by continuation. In: ICCV (2017)Google Scholar
  4. 4.
    Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: BMVC (2014)Google Scholar
  5. 5.
    Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR, pp. 2414–2423 (2016)Google Scholar
  6. 6.
    Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)CrossRefGoogle Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)Google Scholar
  8. 8.
    Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: NIPS, pp. 1042–1050 (2009)Google Scholar
  9. 9.
    Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. In: CVPR, pp. 3270–3278 (2015)Google Scholar
  10. 10.
    Li, W., Wang, S., Kang, W.: Feature learning based deep supervised hashing with pairwise labels. In: IJCAI, pp. 1711–1717 (2016)Google Scholar
  11. 11.
    Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: CVPRW on Deep Vision, pp. 27–35 (2015)Google Scholar
  12. 12.
    Norouzi, M., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: ICML, pp. 353–360 (2011)Google Scholar
  13. 13.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: CVPR, pp. 37–45 (2015)Google Scholar
  15. 15.
    Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR abs/1312.6034 (2013)Google Scholar
  16. 16.
    Wang, J., Kumar, S., Chang, S.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2393–2406 (2012)CrossRefGoogle Scholar
  17. 17.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753–1760 (2008)Google Scholar
  18. 18.
    Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retreieval via image representation learning. In: AAAI, pp. 2156–2162 (2014)Google Scholar
  19. 19.
    Yang, H.F., Lin, K., Chen, C.S.: Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 437–451 (2018)CrossRefGoogle Scholar
  20. 20.
    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(12), 4766–4779 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Zhao, F., Huang, Y., Wang, L., Tan, T.: Deep semantic ranking based hashash for multi-label image retreieval. In: CVPR, pp. 1556–1564 (2015)Google Scholar
  22. 22.
    Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: AAAI, pp. 2415–2421 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan
  2. 2.Institute of Information Science, Academia SinicaTaipeiTaiwan
  3. 3.MOST Joint Research Center for AI Technology and All Vista HealthcareTaipeiTaiwan

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