Fast Image Matching Algorithm Based on Best-Buddies Similarity

An improved image matching algorithm is proposed to solve the problems of high computational complexity and inaccurate target positioning of best-buddies similarity (BBS) image matching algorithm. According to the size of the template image, the size of image blocks is correspondingly selected to reduce the number of points in the matching point set, and then to reduce the computation of the BBS algorithm. The sub blocks arc rearranged according to their gray values, and thus the BBS confidence map of is obtained. The possible location of the target is screened out from the confidence map, and the true BBS score of the possible position of the target is recalculated. The BBS score obtained by bilinear interpolation is replaced by the real BBS score of the recalculated possible location of the target. The location with the highest BBS score among the possible locations is taken as the matching result. Experimental results show that the algorithm improves the accuracy of the target positioning while reducing the running time of the BBS algorithm.