Advertisement

Cascaded Deep Hashing for Large-Scale Image Retrieval

  • Jun Lu
  • Li ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

It is very crucial for large-scale image retrieval tasks to extract effective hash feature representations. Encouraged by the recent advances in convolutional neural networks (CNNs), this paper presents a novel cascaded deep hashing (CDH) method to generate compact hash codes for highly efficient image retrieval tasks on given large-scale datasets. Specifically, we ingeniously utilize three CNN models to learn robust image feature representations on a given dataset, which solves the issue that categories with poor feature representation have a fairly low retrieval precision. Experimental results indicate that CDH outperforms some state-of-the-art hashing algorithms on both CIFAR-10 and MNIST datasets.

Keywords

Image retrieval Convolutional neural networks Hash code Image representation 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants No. 61373093, No. 61402310, No. 61672364 and No. 61672365, by the Soochow Scholar Project of Soochow University, by the Six Talent Peak Project of Jiangsu Province of China, by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. SJCX18_0846), and by the Graduate Innovation and Practice Program of colleges and universities in Jiangsu Province.

References

  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Qiu, G.: Indexing chromatic and achromatic patterns for content-based colour image retrieval. Pattern Recogn. 35(8), 1675–1686 (2002)CrossRefGoogle Scholar
  3. 3.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  4. 4.
    Wan, J., et al.: Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 157–166. ACM (2014)Google Scholar
  5. 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)Google Scholar
  6. 6.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  7. 7.
    Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2014)Google Scholar
  8. 8.
    Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 806–813 (2014)Google Scholar
  9. 9.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006).  https://doi.org/10.1007/11744023_32CrossRefGoogle Scholar
  10. 10.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: International Conference on Very Large Data Bases, pp. 518–529. Morgan Kaufmann Publishers Inc. (1999)Google Scholar
  11. 11.
    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
  12. 12.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2008)Google Scholar
  13. 13.
    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
  14. 14.
    Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems, pp. 1042–1050 (2009)Google Scholar
  15. 15.
    Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: AAAI (2014)Google Scholar
  16. 16.
    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, pp. 27–35 (2015)Google Scholar
  17. 17.
    Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081 (2012)Google Scholar
  18. 18.
    Lecun, Y., Cortes, C.: The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/ (2010)
  19. 19.
    Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report 1 (4), p. 7. University of Toronto (2009)Google Scholar
  20. 20.
    Hellerstein, J.M.: Generalized search tree. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 1222–1224. Springer, Boston (2009).  https://doi.org/10.1007/978-1-4899-7993-3CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and Technology and Joint International, Research Laboratory of Machine Learning and Neuromorphic ComputingSoochow UniversitySuzhouChina
  2. 2.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina

Personalised recommendations