Abstract
In this paper, the performance improvement benefiting from the combination of local feature detectors for image matching and registration is evaluated. Possible combinations of five types of representative interest point detectors and region detectors are integrated into the testing framework. The performance is compared using the number of correspondences and the repeatability rate, as well as an original evaluation criterion named the Reconstruction Similarity (RS), which reflects not only the number of matches, but also the degree of matching error. It is observed that the combination of DoG extremum and MSCR outperforms any single detectors and other detector combinations in most cases. Furthermore, MDSS, a hybrid algorithm for accurate image matching, is proposed. Compared with standard SIFT and GLOH, its average RS rate exceeds more than 3.56%, and takes up even less computational time.
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