Two-step matching strategy combining global-local descriptor

Feature description and matching are at the base of many computer vision applications. However, traditional local descriptors cannot fully describe all information of features, and there are so many feature points and so long local descriptors that the matching steps are time-consuming. In order to solve these problems. This paper proposed a new efficient method for description and matching, called TSMwGLD (the two-step matching with global and local Descriptors). In TSMwGLD, first, it designed a simple global descriptor and then found N best-matching points by using global descriptors, and at the same time it could eliminate lots of points which didn’t match in global information. Next, the method continued the matching step to find the best-matching point by using the local descriptors of N candidate points. So the whole matching process could become faster because the distances between global descriptors with the size of 8 were computed more easily than local descriptors with the size of 64 in SURF. The experimental results show that TSMwGLD results in increased accuracy and faster matching than original method. Especially for blurred images with textures, the matching time is less than tenth of original and the whole description and matching process is about two times faster than SURF.