Abstract: Due to the local ambiguities of images containing repetitive-patterns, it is difficult to match feature points reliably only by comparing similarity between local descriptors even if the disparity of viewpoint is not very large. Thus, a novel representation of geometric consistency named approximate distance order is proposed according to the space distribution of feature points. Then, an object function in hybrid form is defined by combining the matching cost of local descriptor, and the matching problem is formulated as an optimization problem with one-to-one correspondence constraints. Finally, the correspondences between feature points are obtained by maximizing the given object function via the method of probabilistic relaxation. Comparative experiments applied to various images demonstrate the algorithm is an effective approach to solving the suggested problem.