Abstract

Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use estab- lished metrics to embed our assessment into the body of existing evalua- tions, allowing us to provide a novel taxonomy unifying both traditional and novel binary features. Moreover, we analyze the performance of dif- ferent detector and descriptor pairings, which are often used in practice but have been infrequently analyzed. Additionally, we complement exist- ing datasets with novel data testing for illumination change, pure camera rotation, pure scale change, and the variety present in photo-collections. Our performance analysis clearly demonstrates the power of the new class of features. To benefit the community, we also provide a website for the automatic testing of new description methods using our provided metrics and datasets (www.cs.unc.edu/feature-evaluation).