A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably matched and this necessitates descriptor-based learning. We explore factors that influence such learning and registration: the loss function, descriptor type, geometric parametrization and the trade-off between matchability and geometric accuracy and propose a novel hard negative-constant loss function for learning of affine regions. The affine shape estimator -- AffNet -- trained with the hard negative-constant loss outperforms the state-of-the-art in bag-of-words image retrieval and wide baseline stereo. The proposed training process does not require precisely geometrically aligned patches.The source codes and trained weights are available at https://github.com/ducha-aiki/affnet
@article{arxiv.1711.06704,
title = {Repeatability Is Not Enough: Learning Affine Regions via Discriminability},
author = {Dmytro Mishkin and Filip Radenovic and Jiri Matas},
journal= {arXiv preprint arXiv:1711.06704},
year = {2018}
}