Many same or similar line features may hinder line matching in the image, matching only based on the similarity between individual line, which easily lead to the failure of the algorithm. Therefore, this paper proposed a reliable pair-wise line matching algorithm based on feature similarity constraints of geometric attributes between the lines. There are two challenges in constructing robust feature similarity constraints based on pair-wise line matching. The first challenge is to generate grouped line pairs under unstable lines extraction. This algorithm is handled by the basic geometric relationships such as distance and angle between the lines. The second challenge is to design a reliable feature descriptor robust to large viewpoint changes taking into account that the line pairs may not be coplanar and their endpoints are inaccurate. This algorithm salient not only against a range of viewpoint changes for close-range image but also large affine transformation. The construction method is described as follows. Firstly, line segments are extracted by using a line segment detector method and the corresponding points obtained by using SIFT matching points in the reference image and searching image.The algorithm generates grouped line pairs from lines extracted from the reference image and searching image according to the basic geometric relationships such as distance and angle between the lines, and it takes the grouped line pairs as matching primitives. Then the method employs the epipolar constraint to evaluate candidate line pairs. Line matching algorithm based on geometric attributes of lines for descriptor and similarity measure of line pairs are presented which is more distinctive by describing the relationship between every two pairs. The configuration of two line pairs is described by distinguishing two cases, in the first case, the similarity of the two line pairs is called as affine similarity; In another case, it is called general similarity. The affine similarity and general similarity are used as the overall similarity measure in this algorithm and are determined based on the calculated feature vectors between the target line pairs and each candidate line pairs. Instead of screening all candidate line pairs, the optimal line pairs mapping that maximizes the similarity measure between the two line pairs. Finally, the direction constraint which can provide a solution for angle transformation caused by image rotation in line pairs matching is used to perform the correspondence of individual line, it resolves the corresponding line pairs into two pairs of corresponding individual lines, and obtains one to one matching results after the post-processing of checking. Five typical groups of close-range image pairs with angle, rotation, and scale transformation are used as the experimental dataset, which is used to complete the line pairs matching experiments by the proposed algorithm. In comparison with other line matching algorithms, the proposed method can obtain more accurate line matching results in different typical close-range image pairs, and its matching ratio increased by 14.5. The experiment results demonstrate that the effect of threshold selection is weak and the algorithm is robust which achieves reliable line matching results.