Name
Abstract | ||
|---|---|---|
We present an accurate method for estimation of the affine shape of local features. method is trained in a novel way, exploiting the recently proposed HardNet triplet loss. loss function is driven by patch descriptor differences, avoiding problems with symmetries. Moreover, such training process does not require precisely geometrically aligned patches. affine shape is represented in a way amenable to learning by stochastic gradient descent. When plugged into a state-of-the-art wide baseline matching algorithm, the performance on standard datasets improves in both the number of challenging pairs matched and the number of inliers. Finally, AffNet with combination of Hessian detector and HardNet descriptor improves bag-of-visual-words based state of the art on Oxford5k and Paris6k by large margin, 4.5 and 4.2 mAP points respectively. The source code and trained networks are available at this https URL |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Affine transformation,Stochastic gradient descent,Pattern recognition,Source code,Computer science,Hessian matrix,Artificial intelligence,Discriminative model,Detector,Homogeneous space,Blossom algorithm |
DocType | Volume | Citations |
Journal | abs/1711.06704 | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmytro Mishkin | 1 | 175 | 10.20 |
| Filip Radenovic | 2 | 210 | 7.89 |
| Jiri Matas | 3 | 335 | 35.85 |