Advertisement

Image Retrieval Using Inception Structure with Hash Layer for Intelligent Monitoring Platform

  • BaoHua Qiang
  • Xina Shi
  • Yufeng Wang
  • Zhi Xu
  • Wu XieEmail author
  • Xianjun Chen
  • Xingchao Zhao
  • Xukang Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

In view of the problem of low efficiency and accuracy in traditional image retrievals, a method using inception structure with hash layers of image retrieval is presented for intelligent monitoring platform. The main idea of our work is to add hash layers into the inception structure of deep neural network, which can be used to transform the global average pooling features into low dimensional binary hash codes. Our method is utilized to not only ensure the sparseness of the neural network, but also avoid the overfitting phenomenon. Experimental results via the MNIST and CIFAR-10 datasets show that the retrieval efficiency and accuracy can be higher using our methods than before.

Keywords

Image retrieval Inception structure Intelligent platform Deep learning 

Notes

Acknowledgments

This work is supported by the National Marine Technology Program for Public Welfare (No. 201505002), Guangxi Science and Technology Development Project (No. 1598018-6), the National Natural Science Foundation of China under Grant No. 61462020, No. 61762025 and No. 61662014, Guangxi Natural Science Foundation under Grant No. 2017GXNSFAA198226, Guangxi Key Research and Development Program (No. AB17195053), Guangxi Key Laboratory of Trusted Software (kx201510, kx201413), Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System (Nos. 14106, 15204), the Innovation Project of GUET Graduate Education (Nos. 2017YJCX52, 2018YJCX42), Guangxi Cooperative Innovation Center of Cloud Computing and Big Data (Nos. YD16E01, YD16E04, YD1703, YD1712, YD1713, YD1714). Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics (No. GIIP201603).

References

  1. 1.
    Blanco, G., Bedo, M.V., Cazzolato, M.T., et al.: A label-scaled similarity measure for content-based image retrieval. In: IEEE International Symposium on Multimedia, pp. 20–25. IEEE (2016)Google Scholar
  2. 2.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  3. 3.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. IEEE (2015)Google Scholar
  4. 4.
    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE (2016)Google Scholar
  5. 5.
    Chandrasekhar, V., Lin, J., Liao, Q., et al.: Compression of deep neural networks for image instance retrieval. In: Data Compression Conference, pp. 300–309. IEEE (2017)Google Scholar
  6. 6.
    Nikkam, P.S., Reddy, E.B.: A key point selection shape technique for content based image retrieval system. In: International Journal of Computer Vision and Image Processing, pp. 54–70 (2016). IJCVIPGoogle Scholar
  7. 7.
    Chathurani, N., Geva, S., Chandran, V., et al.: Image retrieval based on multi-feature fusion for heterogeneous image databases. Int. J. Comput. Inf. Eng. 10(10), 1797–1802 (2016)Google Scholar
  8. 8.
    Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: Twenty-Eighth AAAI Conference on Artificial Intelligence AAAI (2014)Google Scholar
  9. 9.
    Hershey, S., Chaudhuri, S., Ellis, D.P.W., et al.: CNN architectures for large-scale audio classification. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 131–135. IEEE (2017)Google Scholar
  10. 10.
    Zhang, H., Feng, L., Wu, N., Li, Z.: Integration of learning-based testing and supervisory control for requirements conformance of black-box reactive systems. IEEE Trans. Autom. Sci. Eng. 15(1), 2–15 (2018)CrossRefGoogle Scholar
  11. 11.
    Liong, V.E., Lu, J., Wang, G., et al.: Deep hashing for compact binary codes learning. In: Computer Vision and Pattern Recognition, pp. 2475–2483. IEEE (2015)Google Scholar
  12. 12.
    Salakhutdinov, R., Hinton, G.E.: Semantic hashing. Int. J. Approx. Reason. 50(7), 969–978 (2009)CrossRefGoogle Scholar
  13. 13.
    Lin, K., Yang, H.F., Hsiao, J.H., et al.: Deep learning of binary hash codes for fast image retrieval. In: Computer Vision and Pattern Recognition Workshops, pp. 27–35. IEEE (2015)Google Scholar
  14. 14.
    Jia, Y., Shelhamer, E., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)Google Scholar
  15. 15.
    Lecun, Y., Cortes, C.: The MNIST database of handwritten digits (2010)Google Scholar
  16. 16.
    Krizhevsky, A.: Learning multiple layers of features from tiny images (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • BaoHua Qiang
    • 1
  • Xina Shi
    • 1
  • Yufeng Wang
    • 2
  • Zhi Xu
    • 1
  • Wu Xie
    • 1
    Email author
  • Xianjun Chen
    • 1
  • Xingchao Zhao
    • 1
  • Xukang Zhou
    • 1
  1. 1.Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnologyGuilinChina
  2. 2.The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuangChina

Personalised recommendations