Affnet (detection)

Affnet: Repeatability Is Not Enough: Learning Affine Regions via Discriminability

Abstract: 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.

Tasks: Image Retrieval

Datasets: Oxford5k, HPatches

Conference: ECCV 2018

Licence: MIT

https://raw.githubusercontent.com/ducha-aiki/affnet/master/imgs/graf16HesAffNet.jpg