当图像中特征点缺失比较严重时，利用特征点S维分配算法和K均值聚类算法的图像匹配性能较差。此外，随着待匹配图像数量的增加，上述两类算法的计算量急剧上升。针对上述问题，提出一种新的图像匹配判决算法——利用特征点序列截断的匹配判决。该算法利用了匹配特征点之间的匹配度量大于非匹配特征点之间的匹配度量，以及同一匹配簇中来源于同一图像的特征点不超过一个的先验知识，一方面对特征点缺失具有较强的鲁棒性，另一方面克服了上述两类算法中的组合爆炸问题。仿真实验证实了所提算法的有效性。

The performance of image matching based on S-dimensional assignment algorithm and K-means clustering algorithm are both poor when the feature points within the images are severely missed. Besides, the computational complexity of the aforementioned two algorithms increases dramatically when the number of images to be matched increases. Aiming at these problems, a new decision algorithm for image matching decision is proposed which uses the truncation of the feature point sequence. The algorithm takes advantage of the prior knowledge that the matching measures between the feature points which are matched are larger than those which between the feature points which are not matched. So on the one hand it has good robustness to feature points missing problem, on the other hand it overcomes the combinatorial explosion problem in the aforementioned two algorithms. Simulations validate the effectiveness of the proposed algorithm.