In order to improve the accuracy and efficiency of infrared image matching, a new local feature detection algorithm based on combination of Harris-Laplace and the rotation invariant LBP was proposed. The algorithm could not only get good detection effect when image scale, the light, the angle changed, but also a good description of the local texture features of images. After completion of feature vectors described, in order to further improve the accuracy of infrared image feature points matching, this paper presented an image matching strategy based on K-means clustering analysis. Firstly, Cosine correlation matching strategy was used to achieve initial coarse matching feature points; Secondly, using K-means clustering analysis excluded most of the matching strategy image mismatching points. Experimental results show that the characterization algorithm maintained a good robustness and repetition rate (Repeatability) improved by 9.2%. Compared with the traditional matching algorithm, matching precision matching strategy based K-means clustering analysis can be increased by 5.05%, matching time can be reduced by 0.068s. In this paper, the feature description algorithm and matching algorithm based on K-means clustering analysis could meet the high precision and high real-time requirements of image registration.