A SIFT Feature Matching Algorithm Based on Semi-Variance Function
Feng Tian, Yu Bo Yan · Advanced materials research · 2013
For solving the low matching efficiency problem due to high dimension of eigenvector in SIFT, a SIFT feature matching algorithm based on semi-variance function is proposed. For each feature point in image SIFT feature point zone, m beams are generated by using the position of the feature point as center and the orientation of the feature point as start direction. The image semi-variance function value of each beam, which is treated as SIFT value of eigenvector descriptor, is used in the algorithm aiming at reducing the dimension of eigenvector and improving image matching efficiency. The experiment result shows that the matching rate of this algorithm is higher, the matching time of this algorithm is less.