一种基于最佳伙伴相似性的快速图像匹配算法

Fast Image Matching Algorithm Based on Best-Buddies Similarity

针对最佳伙伴相似性(BBS)图像匹配算法计算复杂度高、目标定位不准确等问题,提出了一种改进的图像匹配算法。根据模板图像的尺寸自适应选择图像分块大小,以减少匹配点集中点的数目,从而减小BBS算法的运算量;根据子块的灰度值重新排列子块,在此基础上得到BBS的置信度图,从置信度图中筛选出目标的可能位置,并重新计算目标可能位置的真实BBS分数;用目标可能位置的真实BBS分数替换通过双线性插值得到的BBS分数,将目标可能位置中BBS分数最高的位置作为匹配结果。实验结果表明,该算法可降低BBS算法的运行时间,同时提高目标定位的准确度。

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 are 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.