An Improved ORB Image Matching Algorithm Based on Compressed Sensing

  • Yijie Wang
  • Songlin GeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


Aiming at the problems such as large amount of computation, high complexity and slow speed in feature extraction of the existing algorithms, this paper presents an improved ORB image matching algorithm based on compressed sensing. Firstly, compressed sensing is used to compress the target image and the matched image, and obtain sparse matrices of wavelet coefficient respectively. Secondly, the ORB algorithm is used to extract the feature points of the image. Finally, the KNN algorithm is used as a matching strategy to perform image matching. Experimental results show that the algorithm realizes fast image matching and guarantees the matching accuracy.


Image matching Compressed sensing ORB algorithm KNN 



The work was supported by the National Natural Science Foundation of China (No. 61762037), Science and Technology Project of Jiangxi Provincial Transport Bureau (No. 2016D0037) and Innovation Fund Designated for Graduate Students of Jiangxi Province (No. YC2017-S253).


  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Bradski, G.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  3. 3.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, pp. 2564–2571. IEEE (2011)Google Scholar
  4. 4.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Karami, E., Prasad, S., Shehata, M.: Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. In: Proceedings of the 2015 Newfoundland Electrical and Computer Engineering Conference, St. Johns, Canada (2015)Google Scholar
  6. 6.
    Ma, D., Yu, Z., Yu, J., Pang, W.: A novel object tracking algorithm based on compressed sensing and entropy of information. Math. Probl. Eng. 2015, 18 (2015). Article ID 628101. Scholar
  7. 7.
    Liu, J., Li, X.C., Zhu, K.J., et al.: Distributed compressed sensing based remote sensing image fusion algorithm. J. Electron. Inf. Technol. 39(10), 2374–2381 (2017)Google Scholar
  8. 8.
    Li, X.L., Xie, C.M., Jia, Y.X., et al.: Fast object detection algorithm based on ORB feature. J. Electron. Meas. Instrum. 5, 455–460 (2013)Google Scholar
  9. 9.
    Liu, W., Zhao, W.J., Li, D.J., et al.: A feature point matching algorithm based on ORB detection. Laser Infrared 45(11), 1380–1384 (2015)Google Scholar
  10. 10.
    Liu, T., Zhang, J.: Improved image stitching algorithm based on ORB features by UAV remote sensing. Comput. Eng. Appl. 54(2), 193–197 (2018)Google Scholar
  11. 11.
    Larose, D.T.: k-nearest neighbor algorithm. In: Larose, D.T. (ed.) Discovering Knowledge in Data: An Introduction to Data Mining, pp. 90–106. Wiley, Hoboken (2005)CrossRefGoogle Scholar
  12. 12.
    Xie, X., Xu, Y., Liu, Q., et al.: A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform. J. Ambient Intell. Humaniz. Comput. 6(6), 835–843 (2015)CrossRefGoogle Scholar
  13. 13.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: European Conference on Computer Vision, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: binary robust independent elementary features. In: European Conference on Computer Vision, pp. 778–792. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Rosin, P.L.: Measuring corner properties. Comput. Vis. Image Underst. 73(2), 291–307 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information EngineeringEast China Jiaotong UniversityNanchangChina

Personalised recommendations