In many computer vision tasks

one of the core steps is to set up reliable correspondences of points between two images. Although image matching methods based on local descriptors have been well studied

they are usually unable to find the correct corresponding points of the images containing repetitive patterns even if the viewpoint changes are very small. Due to the local information ambiguities of the images containing repetitive patterns

false matches can be easily produced by the local feature based image matching algorithms. Meanwhile

the matching algorithms combining with the global feature still depend on the main orientation which is obtained by calculating the local information. Therefore

these algorithms also usually lead to mismatching for the images with repetitive patterns. Thus

it is meaningful to cope with the challenging matching task since such repetitive patterns widely exist in the real world images of artificial objects or scenes. To solve this problem

a novel image matching algorithm based on pair-wise feature points is proposed in this paper. First

FAST detector is adopted to estimate the locations of the feature points. It is an effective and efficient method for feature detection. Then

the direction vector between the pair-wise points is utilized to be the main orientation

which provides the right direction for both the local and global feature description. In addition

local DAISY descriptor and the improved global context descriptor are used in the proposed algorithm to improve the matching ability. We evaluate the proposed method on both the simulative and real images against several state-of-the-art algorithms. For the simulative images experiments

the proposed method outperforms than other ones on the mean and the standard deviation of the matching accuracy. For the real images experiments

the test datasets contain the stereo matching images and the remote sense images. On the average matching correct rate

the proposed algorithm can reach more than 88% and increase at least 26% more than the other classical matching methods. Experiments on images of both simulative and real as well as comparisons with the state-of-the-art methods have demonstrated the effectiveness and robustness of the proposed method. Moreover

the proposed algorithm is an effective approach to solve the repetitive patterns images matching problem.