Skip to main content
SpringerLink
Log in

A novel deep hashing method for fast image retrieval

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

In recent years, the deep hashing image retrieval algorithm has become a hot spot in current research. Although the deep hashing algorithm has achieved good results in image retrieval, how to further improve the accuracy of the image retrieval algorithm and reduce the computational complexity of the algorithm, the two basic requirements of the algorithm, need attention in image retrieval. The paper proposes a new Aggregate Deep Fast Supervised Discrete Hashing (ADFSDH) method for highly efficient image retrieval on large-scale datasets. Specifically, in order to improve the algorithm performance, the paper first proposes a new Aggregate Deep Convolutional Neural Network (ADCNN) mode based on VGG16, VGG19 and transfer learning for effective image feature extraction, which contains two different feature extractors in parallel. And then, the paper proposes a new feature fusion method. When our weighted proportion is consistent with the Mean Average Precision results of two different feature extractors, we can obtain the most accurate description of the image. Firstly, in order to save ADCNN required storage space and improve ADCNN image retrieval efficiency, the Fast Supervised Discrete Hashing algorithm after adjusting the parameters is introduced into the ADCNN model. In addition, ADFSDH unifies feature learning and hash coding into the same framework. The proposed method was experimented on three datasets (CIFAR10, MNIST and FD-XJ), and the result shows that it is superior to the current mainstream approaches in image retrieval.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Oliva, A., Torralba, A.: Building the gist of a scene: the role of global image features in recognition. Prog. Brain Res. 155, 23–36 (2006)

    Article  Google Scholar 

  2. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  3. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 886–893 (2005)

  4. LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  5. 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)

  6. Deng, J., Dong, W., Socher, R., et al.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255 (2009)

  7. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)

  8. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

  9. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: VLDB, vol. 99(6), pp. 518–529 (1999)

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

  11. Liu, W., Wang, J., Ji, R., et al.: Supervised hashing with kernels. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 2074–2081 (2012)

  12. Shen, F., Shen, C., Liu, W., et al.: Supervised Discrete Hashing. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), vol. 2(3), pp. 37–45 (2015)

  13. Gui, J., Liu, T., Sun, Z., et al.: Supervised discrete hashing with relaxation. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 1–10 (2016)

    Google Scholar 

  14. Gui, J., Liu, T., Sun, Z., et al.: Fast supervised discrete hashing. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 490–496 (2018)

    Article  Google Scholar 

  15. Xia, R., Pan, Y., Lai, H., et al.: Supervised hashing for image retrieval via image representation learning. In: AAAI (2014)

  16. Alzu’bi, A., Amira, A., Ramzan, N.: Compact root bilinear CNNs for content-based image retrieval. In: ICIVC, pp. 41–45 (2016)

  17. Alzu’bi, A., Amira, A., Ramzan, N.: Content-based image retrieval with compact deep convolutional features. Neurocomputing 249, 95–105 (2017)

    Article  Google Scholar 

  18. Huang, H.K., Chiu, C.F., Kuo, C.H., et al.: Mixture of deep CNN-based ensemble model for image retrieval. In: Proceedings of 5th Global Conference on Consumer Electronics, pp. 1–2 (2016)

  19. Zhong, G., Xu, H., Yang, P., et al.: Deep hashing learning networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 2236–2243 (2016)

  20. Li, J., Li, J.: Supervised hashing binary code with deep CNN for image retrieval. In: Proceedings of 8th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 649–655 (2015)

  21. Lai, H., Pan, Y., Liu, Y., et al.: Simultaneous feature learning and hash coding with deep neural networks, pp. 3270–3278 (2015)

  22. Peng, T., Li, F.: Image retrieval based on deep Convolutional Neural Networks and binary hashing learning. In: IEEE International Conference on Speech and Signal Processing (ICASSP), pp. 1742–1746 (2017)

  23. Liu, H., Wang, R., Shan, S., et al.: Deep supervised hashing for fast image retrieval. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 2064–2072 (2016)

  24. Gong, Y., Lazebnik, S., Gordo, A., et al.: 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)

    Article  Google Scholar 

  25. Norouzi, M., Blei, D.M.: Minimal loss hashing for compact binary codes. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 353–360 (2011)

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

  27. Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2393–2406 (2012)

    Article  Google Scholar 

  28. Lin, K., Yang, H.F., Hsiao, J.H., et al.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 27–35 (2015)

  29. Li, K., Qi, G.J., Ye, J., et al.: Linear subspace ranking hashing for cross-modal retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1825–1838 (2017)

    Article  Google Scholar 

  30. Jiang Q.Y., Li, W.J.: Deep cross-modal hashing. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 3270–3278 (2017)

  31. Zheng, Y.T., Neo, S.Y., Chua, T.S., et al.: Toward a higher-level visual representation for object-based image retrieval. Vis. Comput. 25(1), 13–23 (2009)

    Article  Google Scholar 

  32. Lavoué, G.: Combination of bag-of-words descriptors for robust partial shape retrieval. Vis. Comput. 28(9), 931–942 (2012)

    Article  Google Scholar 

  33. Joia, P., Gomez-Nieto, E., Neto, J.B., et al.: Class-specific metrics for multidimensional data projection applied to CBIR. Vis. Comput. 28(10), 1027–1037 (2012)

    Article  Google Scholar 

  34. Li, H., Toyoura, M., Shimizu, K., et al.: Retrieval of clothing images based on relevance feedback with focus on collar designs. Vis. Comput. 32(10), 1–13 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Chinese National Natural Science Foundation (Program Nos. 61471311, 61771416), the Creative Research Groups of Higher Education of Xinjiang Uygur Autonomous Region (Program No.XJEDU2017T002) and the Saier Network Next Generation Internet Technology Innovation Project (Program No.NGII20170325).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Xinjiang University, No. 666, Victory Rd., Tianshan District, Urumchi, 830046, China

    Shuli Cheng & Huicheng Lai

  2. School of Software Engineering, Xinjiang University, No. 499, Northwest Rd., Saybagh District, Urumchi, 830046, China

    Liejun Wang

  3. School of Education, Shaanxi Normal University, No. 199, Changan South Rd., Yanta District, Urumchi, Xi’an, 710062, China

    Jiwei Qin

Authors
  1. Shuli Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huicheng Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liejun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiwei Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huicheng Lai.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, S., Lai, H., Wang, L. et al. A novel deep hashing method for fast image retrieval. Vis Comput 35, 1255–1266 (2019). https://doi.org/10.1007/s00371-018-1583-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-018-1583-x

Keywords

Search

Navigation