目的 图像的精确匹配在图像处理与识别中起着重要的作用。为了提高图像的匹配效果，提出了一种迭代的图变换匹配算法来实现误匹配关系的去除从而提高图像的匹配精度。方法 首先利用传统的图变换匹配（GTM）算法从初始匹配关系集合中获得较为精确的匹配关系子集；然后，利用已经获得的正确匹配点集与初始匹配点集之间的几何关系对初始匹配进行修正；最后，利用GTM对修正后的匹配关系进一步优化，从而得到更多的精确匹配关系。结果 实验结果显示在不同的图像变换场景下，相比于传统GTM算法，该算法具有较高的查全率。结论 所提算法能够克服传统GTM算法所得正确匹配关系少的缺陷。

Objective Image matching is an important technique in image processing and recognition. In order to enhance the matching effectiveness and to obtain accurate matches,an iterative graph transformation matching algorithm is proposed in this paper to remove the error matches, which usually exist in the initial matches obtained by image features such as scale invariant feature transform (SIFT) and speeded up robust features (SURF). Method Generally,the proposed algorithm carries out the following three steps: first, the algorithm generates the accurate correspondences from the initial one-to-one correspondence set by using the traditional graph transformation matching algorithm,whose process is similar to that in graph transformation matching (GTM) algorithm; then,it further revises the initial correspondences by using the geometric relationship between the obtained correct matches and the initial matches; finally,based on the revised initial matches,the algorithm further searches the correct matches from the revised initializations by using graph transformation matching algorithm. Compared with GTM,the proposed algorithm further explores the geometric relationship in the matching process and thus returns more accurate matches. Result Experimental results on real-world image matching shows that under various image transformation scenes,the proposed algorithm can significantly outperform GTM on the matching recall while retain the similar high matching precision. Conclusion The proposed iterative graph transformation matching algorithm can overcome the main drawback of GTM that usually returns small number of precise matches.