Increasing the accuracy of feature evaluation benchmarks using differential evolution

Abstract

The accuracy evaluation of image feature detectors is done using the repeatability criterion. Therefore, a well-known data set consisting of image sequences and homography matrices is processed. This data serves as ground truth mapping information for the evaluation and is used in many computer vision papers. An accuracy validation of the benchmarks has not been done so far and is provided in this work. The accuracy is limited and evaluations of feature detectors may result in erroneous conclusions. Using a differential evolution approach for the optimization of a new, feature-independent cost function, the accuracy of the ground truth homographies is increased. The results are validated using comparisons between the repeatability rates before and after the proposed optimization. The new homographies provide better repeatability results for each detector. The repeatability rate is increased by up to 20%.