A Large-Scale Image Retrieval Method Based on Image Elimination Technology and Supervised Kernel Hash

  • Zhiming YinEmail author
  • Jianguo Sun
  • Xingjian Zhang
  • Liu Sun
  • Hanqi Yin
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)


The Internet develops rapidly in the era of big data, which can be shown by the widespread uses of image processing software as well as digital images skills. However, there are a large number of redundant images in the network, which not only occupy the network storage but also slow down image search speed. At the same time, the image hash algorithm has received extensive attention due to its advantages of improving the image retrieval efficiency while reducing storage space. Therefore, this paper aims to propose a large-scale image retrieval method based on image redundancy and hash algorithm for large-scale image retrieval system with a large number of redundant images. I look upon the method into two phases: The first phase is eliminating the redundancy of repetitive images. As usual, image features need to be extracted from search results. Next, I use the K-way, Min-Max algorithm to cluster and sort the returned images and filter out the image classes in the end to improve the speed and accuracy of the image retrieval. Fuzzy logic reasoning comes to the last part. It can help to select the centroid image so as to achieve redundancy. The second phase is image matching. In this stage, the supervised kernel hashing is used to supervise the deep features of high-dimensional images and the high-dimensional features are mapped into low-dimensional Hamming space to generate compact hash codes. Finally, accomplish the efficient retrieval of large-scale image data in low-dimensional Hamming of the space. After texting three common dataset, the preliminary results show that the computational time can be reduced by the search image redundancy technology when filter out the invalid images. This greatly improves the efficiency of large-scale image retrieval and its image retrieval performance is better than the current mainstream method.


Image retrieval Image redundancy Fast matching Supervised kernel hashing Fuzzy logic inference 


  1. 1.
    Yang, X., Zhu, Q., Cheng, K.T.: Near-duplicate detection for images and videos. In: ACM Workshop on Large-Scale Multimedia Retrieval and Mining, pp. 73–80. ACM (2009)Google Scholar
  2. 2.
    Pedro, J.S., Siersdorfer, S., Sanderson, M.: Content redundancy in YouTube and its application to video tagging. ACM Trans. Inf. Syst. 29(3), 13–43 (2011)CrossRefGoogle Scholar
  3. 3.
    Indyk, P., Levi, R., Rubinfeld, R.: Erratum for: approximating and testing k-histogram distributions in sub-linear time. In: ACM Symposium on Principles of Database Systems, pp. 343–343. ACM (2015)Google Scholar
  4. 4.
    Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing. IEEE Comput. Soc. 34(6), 1092–1104 (2012)Google Scholar
  5. 5.
    Datar, M., Immorlica, N., Indyk, P., et al.: Locality-sensitive hashing scheme based on p-stable distributions. In: Twentieth Symposium on Computational Geometry, pp. 253–262. ACM (2004)Google Scholar
  6. 6.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: International Conference on Neural Information Processing Systems, pp. 1753–1760. Curran Associates Inc. (2008)Google Scholar
  7. 7.
    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
  8. 8.
    Chang, S.F.: Supervised hashing with kernels. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081. IEEE Computer Society (2012)Google Scholar
  9. 9.
    Nesterov, Y.: Introductory lectures on convex optimization. Appl. Optim. 87(5), xviii, 236 (2004)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Deng, J., Dong, W., Socher, R., ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  11. 11.
    Li, F.F., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: Conference on Computer Vision and Pattern Recognition Workshop, p. 178. IEEE Computer Society (2004)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Zhiming Yin
    • 1
    Email author
  • Jianguo Sun
    • 1
  • Xingjian Zhang
    • 1
  • Liu Sun
    • 1
  • Hanqi Yin
    • 1
  1. 1.College of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

Personalised recommendations