Abstract: When matching images with scale invariant feature transform(SIFT),the Euclidean distance between feature vectors is used as the similarity measurement.But it was difficult to get the best distance ratio.Moreover,when the ratio was a constant,there would be some problems of error matching or matching leakage.Deal with the problem,the Random Sample Consensus (RANSAC) algorithm was introduced.Optimize the ratio in the process adaptively,and we can get the best threshold.SIFT-based image matching algorithm was analyzed,and a bi-direction matching was used to improve the accuracy of image matching and ensure the correctness of matching at maximum level.Finally,the experiment results show that the proposed methods can obtain an optimal threshold for different images.It can get the most ma-tching points and a better matching rate,and by bi-direction matching,better results can be got.

Key words: SIFT,RANSAC,Adaptively,Matching threshold,Bi-direction