This paper presents a novel geometric verification approach coined Fast Sequence Order Re-sorting Technique (F-SORT), capable of rapidly validating matches between images under arbitrary viewing conditions. By using a fundamental framework of re-sorting image features into local sequence groups for geometric validation along different orientations, we simulate the enforcement of geometric constraints within each sequence group in various views and rotations. While conventional geometric verification (e.g. RANSAC) and state-of-the-art fully affine invariant image matching approaches (e.g. ASIFT) are high in computational cost, our approach is multiple times less computational expensive. We evaluate F-SORT on the Stanford Mobile Visual Search (SMVS) and the Zurich Buildings (ZuBuD) image databases comprising an overall of 9 image categories, and report competitive performance with respect to PROSAC, RANSAC and ASIFT. Out of the 9 categories, F-SORT wins PROSAC in 9 categories, RANSAC in 8 categories and ASIFT in 7 categories, with a significant reduction in computational cost of over nine-fold, thirty-fold and hundred-fold respectively.
CITATION STYLE
Chan, J., Lee, J. A., & Qian, K. (2017). F-SORT: An alternative for faster geometric verification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10111 LNCS, pp. 385–399). Springer Verlag. https://doi.org/10.1007/978-3-319-54181-5_25
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