A Local Neighborhood Constraint Method for SIFT Features Matching

  • Qingliang Li
  • Lili Xu
  • Pengliang Zheng
  • Fei He
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


For improving the accuracy of the SIFT matching algorithm with low time cost, this paper proposes a novel matching algorithm which is based on local neighborhood constraints, that is, SIFT matching feature is optimized by the local neighborhood constraint method in the SIFT algorithm. We optimize the matching results by using the information of SIFT feature descriptor and the relative position information of SIFT feature, then the final matching result obtained by RANSANC algorithm to filter the false matched pairs. The experimental results show that our method can improve the accuracy of the matching feature pairs without affecting the time cost.


Image matching SIFT algorithm Local neighborhood constraints 



This work was supported by the Science & Technology Development Program of Jilin Province, China (Nos. 20140101182JC, 20150101060JC, 20150307030GX, 2015Y059 and 20160204048GX), and by the International Science and Technology Cooperation Program of China (Nos. 20140101182JC, 20150101060JC, 20150307030GX and 20160204048GX).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Qingliang Li
    • 1
  • Lili Xu
    • 1
  • Pengliang Zheng
    • 1
  • Fei He
    • 1
  1. 1.Changchun University of Science and Technology, School of Computer Science and TechnologyChangchunChina

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