A Speeded Up Robust Feature Matching Optimization Based on Spatial Constraint

An optimization algorithm based　on Spatial Constraint for Speeded Up Robust Feature (SURF) matching is proposed, called SC-SURF. First, SURF is used for the image feature point detection and matching. Then the matched points are ranked according to the principle that the lower is the ratio of the nearest neighbor, the higher is the matching accuracy. A new coordinate system is created based on the optimal matched points. Every pair of matched points is encoded using the relative spatial map. At the same time, representative data sets are constructed to simplify RANndom SAmple Consensus (RANSAC) by using a minimal number of optimal matches. The target homographic matrix is fitted based on the representative data set. Finally, the spatial verification is performed using the relative spatial map among the weighted matched points and simplified RANSAC. Experiments demonstrate that SC-SURF algorithm achieves good robustness and high speed while maintaining high matching accuracy.