HPatches: A benchmark and evaluation of handcrafted and learned local
descriptors
Abstract
In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the
existing datasets and evaluation protocols do not specify
unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due
to the recent improvements in local descriptors obtained by
learning them from large annotated datasets. Therefore, we
introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval
and classification. This allows for more realistic, and thus
more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-theart descriptors and analyse their properties. We show that
a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation