The existing multi-scale Harris operator has a complex algorithm,large computation amount and low accuracy.To solve the problems,an efficient and simple algorithm is proposed.Firstly,Gaussian kernel function is used to build a multi-scale space for images,and then Harris operator is used to detect feature points in the scale space.The simplified 32-dimension SIFT feature vector is utilized to characterize the feature points.Then,the nearest neighbor method is used for feature matching,and the modified similar triangles method is used to screen the matching points.The improved K-means algorithm is used to group the feature points,so that the feature points within the same group are clustered and the feature points belonging to different groups are far apart.Finally,the modified RANSAC algorithm is used to calculate the transform matrix between the two images.The feature points from different groups are selected,so as to avoid the selected feature points being too close to each other and the algorithm falling into local optimum.The experiments verify the algorithm‘s performance.