Advertisement

Deep Clustering and Block Hashing Network for Face Image Retrieval

  • Young Kyun Jang
  • Dong-ju Jeong
  • Seok Hee Lee
  • Nam Ik ChoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11366)

Abstract

This paper presents a new hashing method to learn the compact binary codes for implementing a large-scale face image retrieval system. Since it is very difficult to deal with the inter-class similarities (similar appearance between different persons) and intra-class variations (same person with different pose, facial expressions, illuminations) in face-related problems, we propose a new deep clustering and block hashing (DCBH) approach to alleviate these issues. The network we adopt for the feature extraction is the VGG, where we design a new loss function to learn the robust and mulit-scale facial features for addressing the above-stated problems. Specifically, we design a center-clustering loss term to minimize the distance between the image descriptors belonging to the same class. Besides, the classification errors of the image descriptors and the learned binary codes are minimized to learn the discriminative binary codes. In addition, we introduce a block hashing layer for reducing the redundancy among hash codes and the number of parameters simultaneously without loss of similarity. Extensive experiments on two large scale face image datasets demonstrate that our proposed method outperforms the state-of-the-art face image retrieval methods.

Keywords

Center-clustering loss Block hashing layer 

Notes

Acknowledgements

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 1711075689, Decentralised cloud technologies for edge/IoT integration in support of AI applications).

References

  1. 1.
    Babenko, A., Lempitsky, V.: Aggregating local deep features for image retrieval. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1269–1277 (2015)Google Scholar
  2. 2.
    Banaeeyan, R., Lye, H., Fauzi, M.F.A., Karim, H.A., See, J.: Semantic facial scores and compact deep transferred descriptors for scalable face image retrieval. Neurocomputing 308, 111–128 (2018)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)Google Scholar
  5. 5.
    Gordo, A., Almazán, J., Revaud, J., Larlus, D.: Deep image retrieval: learning global representations for image search. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 241–257. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46466-4_15CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  7. 7.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
  8. 8.
    Jain, H., Zepeda, J., Pérez, P., Gribonval, R.: SUBIC: a supervised, structured binary code for image search. In: Proceedings of International Conference on Computer Vision, vol. 1, p. 3 (2017)Google Scholar
  9. 9.
    Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117–128 (2011)CrossRefGoogle Scholar
  10. 10.
    Jeong, D.j., Choo, S., Seo, W., Cho, N.I.: Regional deep feature aggregation for image retrieval. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1737–1741. IEEE (2017)Google Scholar
  11. 11.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  12. 12.
    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
  13. 13.
    Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. arXiv preprint arXiv:1504.03410 (2015)
  14. 14.
    Lin, J., Li, Z., Tang, J.: Discriminative deep hashing for scalable face image retrieval. In: Proceedings of International Joint Conference on Artificial Intelligence (2017)Google Scholar
  15. 15.
    Liu, H., Wang, R., Shan, S., Chen, X.: Deep supervised hashing for fast image retrieval. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2064–2072 (2016)Google Scholar
  16. 16.
    Ng, H.W., Winkler, S.: A data-driven approach to cleaning large face datasets. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 343–347. IEEE (2014)Google Scholar
  17. 17.
    Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)Google Scholar
  18. 18.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar
  19. 19.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  20. 20.
    Tang, J., Li, Z., Zhu, X.: Supervised deep hashing for scalable face image retrieval. Pattern Recogn. 75, 25–32 (2018)CrossRefGoogle Scholar
  21. 21.
    Tang, J., Lin, J., Li, Z., Yang, J.: Discriminative deep quantization hashing for face image retrieval. IEEE Trans. Neural Netw. Learn. Syst. (2018)Google Scholar
  22. 22.
    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)CrossRefGoogle Scholar
  23. 23.
    Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46478-7_31CrossRefGoogle Scholar
  24. 24.
    Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 529–534. IEEE (2011)Google Scholar
  25. 25.
    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
  26. 26.
    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 Switzerland AG 2019

Authors and Affiliations

  • Young Kyun Jang
    • 1
  • Dong-ju Jeong
    • 1
  • Seok Hee Lee
    • 1
  • Nam Ik Cho
    • 1
    Email author
  1. 1.Department of Electrical and Computer Engineering INMCSeoul National UniversitySeoulKorea

Personalised recommendations