Improving the matching precision of SIFT

Abstract

We evaluate and improve the matching precision of the SIFT method [1], defined as the root mean square error (RMSE) under a ground truth geometric transform. We first argue that the matching precision reflects to some extent the average relative localization precision between two images. For scale invariant feature detectors like SIFT, we show that the matching precision decreases with the scale of the keypoints, and that this is caused by the scale space sub-sampling in SIFT. We verify that canceling this sub-sampling therefore improves drastically the matching precision. Yet, in case of scale change, this improvement is marginal due to the coarse scale quantization in the scale space. A more sophisticated method is therefore also proposed to improve the matching precision even in case of scale change. This incremented precision is a key ingredient in many important image processing tasks requiring the best precision, such as registration, stitching, and camera calibration.