Repeatability Is Not Enough: Learning Affine Regions via Discriminability

ECCV 2018  ·  Dmytro Mishkin, Filip Radenovic, Jiri Matas ·

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

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Results from the Paper


Ranked #5 on Image Matching on IMC PhotoTourism (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Image Matching IMC PhotoTourism DoG-AffNet-HardNet8 mean average accuracy @ 10 0.64212 # 5

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