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.