A novel geometric filter for affine invariant features

Invariant local image features have proven to be very successful in computer vision tasks involving partial occlusion and various image deformations. Even though the image features can be extracted in a high repeatability, their local appearance alone usually does not bring enough discriminative power to support a reliable matching, resulting in a relatively high number of outliers in the correspondence set. To reject these mismatches, various geometric filters have been proposed for different image features. In this paper, we present a novel and efficient geometric filter for the state-of-the-art affine invariant features. The proposed method detects the mismatches by examining the consistency of local affine geometry between neighboring matches of affine invariant features. Experimental results show that the proposed geometric filter not only achieves a higher inlier ratio than the standard Hough clustering, but also presents superior robustness to severe clutters, significant viewpoint changes and non-rigid deformation.