A METHOD OF SIFT FEATURE POINTS MATCHING FOR IMAGE MOSAIC

This paper presents a approach of SIFT feature points matching for image mosaic. This method combines improved K-means clustering and simulated annealing algorithm to match SIFT feature points. Firstly, high robust points are extracted by SIFT algorithm; Secondly, cluster with the initial centers obtained by density function, and then optimize the results of clustering which are used as initial results of simulated annealing algorithm by perturbation; Thirdly, match feature points according to Nearest Neighbor algorithm; Finally, calculate the homography and realize image mosaic. This method does not need to traverse all feature points and avoid trapping in a local extremum. Experimental results prove that the method is only relative to geometric position of feature points, and is robust on scale invariant, arbitrary rotation and scaling.