BALG: An alternative for fast and robust feature matching

Robust feature matcher is usually inapplicable to real time computer vision applications as its high computational complexity, while fast feature matcher is short of robustness to geometric transformations. By studying the sampling pattern of binary descriptor and local geometric statistics of features, this paper proposes a fast and robust feature matching method with binary affine invariant descriptor and local geometric consistency check (BALG). The sampling pattern is adaptively adjusted according local affine transformation that can be effectively estimated from the local intensity moments. The affine sampling pattern improves the affine invariance of binary descriptor while enable fast processing. Furthermore, candidate matches are sorted into local groups along different orientations with sequence order constraint, and then followed by local geometric consistency check. False matches are efficiently filtered out with high recall. Extensive experiments on four publicly benchmark datasets prove the proposed method to be an alternative for time critical feature matcher.