A comprehensive evaluation of local detectors and descriptors

Abstract As local detectors and descriptors can find and represent distinctive keypoints in an image, various types of keypoints detection and description methods have been proposed. Each method has particular advantages and limitations and may be appropriate in different contexts. In this paper, we evaluate the performance of a wide set of local detectors and descriptors. First, we compare diverse local detectors with regard to the repeatability, and local descriptors in terms of the recall and precision. Next, we apply the visual words model constructed from the local descriptors with real values and binary string to large scale image search. The evaluation results reveal some strengths and weaknesses of the recent binary string descriptors compared with the notable real valued descriptors. Finally, we integrate the local detectors and descriptors with the framework of fully affine space and evaluate their performance under major viewpoint transformations. The presented comparative experimental studies can support researchers in choosing an appropriate local detector and descriptor for their specific computer vision applications.