HPatches: A benchmark and evaluation of handcrafted and learned local descriptors

VAVedaldi, ABVBalntas, VLKLenc, KKMKrystian MikolajczykTTTuytelaars, T

Imperial College London · University of Oxford

Abstract

In this paper, a novel benchmark is introduced for evaluating local image descriptors. We demonstrate limitations of the commonly used datasets and evaluation protocols, that lead to ambiguities and contradictory results in the literature. Furthermore, these benchmarks are nearly saturated due to the recent improvements in local descriptors obtained by learning from large annotated datasets. To address these issues, 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 verification. This allows for more realistic, thus more reliable comparisons in different application…

Citation impact

773
total citations
FWCI
34.50
Percentile
100%
References
45
Citations per year

Authors

6

Topics & keywords

Keywords
  • Benchmark (surveying)
  • Computer science
  • Artificial intelligence
  • Machine learning
  • Matching (statistics)
  • Data mining
  • Mathematics
UN Sustainable Development Goals
  • Quality Education
No related works found for this paper.

Funding