A Local Neighborhood Constraint Method for SIFT Features Matching

Qingliang Li, Lili Xu, Pengliang Zheng and Fei He ()
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Qingliang Li: Changchun University of Science and Technology, School of Computer Science and Technology
Lili Xu: Changchun University of Science and Technology, School of Computer Science and Technology
Pengliang Zheng: Changchun University of Science and Technology, School of Computer Science and Technology
Fei He: Changchun University of Science and Technology, School of Computer Science and Technology

Chapter Chapter 34 in Recent Developments in Data Science and Business Analytics, 2018, pp 313-320 from Springer

Abstract: Abstract 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.

Keywords: Image matching; SIFT algorithm; Local neighborhood constraints (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-72745-5_34

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