An improved SIFT matching algorithm based on locality preserving projection LPP
Peng Cao, Ting Rui, Jinlin Zhang, You Zhou · 2012
The problems of high dimensional feature vectors lead to large data calculating and time consuming in Scale Invariant Feature Transform (SIFT) feature matching. This paper combines the SIFT and Locality Preserving Projection (LPP) as the LPP-SIFT algorithm. First, use the SIFT algorithm to extract feature point vectors, then by the linear LPP dimensionality reduction method, reduce the amount of data. Use the Euclidean distance similarity criteria to achieve the coarse matching feature point vectors. Then re-match each pair of the match points by the way of the Gaussian neighborhood weighted average to get rid of the mismatch points. The experimental results show that the improved LPP-SIFT algorithm can reduce the amount of data, enhance the real-time and has a high matching accuracy and matching efficiency.