Open AccessA novel image matching algorithm based on PCA and SIFT
Author(s) -
Wenju Li,
Xinyuan Na,
Pan Su,
Yunfan Lu,
Tianzhen Dong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012099
Subject(s) - scale invariant feature transform , ransac , matching (statistics) , pattern recognition (psychology) , artificial intelligence , euclidean distance , feature (linguistics) , image (mathematics) , principal component analysis , computer science , dimension (graph theory) , computer vision , blossom algorithm , mathematics , linguistics , statistics , philosophy , pure mathematics
In order to solve the problem of low matching precision and slow matching speed of image matching algorithm based on classical SIFT, a new image matching algorithm based on PCA, SIFT and improved RANSAC is proposed. Firstly, the SIFT feature was extracted from images; Secondly, principal component analysis is used to reduce the dimension of SIFT feature descriptor, from 128 to 20; then, the EUCLIDEAN distance is used for feature matching; finally, an improved RANSAC algorithm is proposed to eliminate the mismatched feature points. Experimental results show that the proposed algorithm improves the accuracy and speed of image matching.