Learning Invariant Representations of Planar Curves
Abstract
We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Eucledian and Similarity group of transformations. We leverage on the representational power of convolutional neural networks to compute these geometric quantities. In comparison with axiomatic constructions, we show that the invariants approximated by the learning architectures have better numerical qualities such as robustness to noise, resiliency to sampling, as well as the ability to adapt to occlusion and partiality. Finally, we develop a novel multi-scale representation in a similarity metric learning paradigm.
Cite
Text
Pai et al. "Learning Invariant Representations of Planar Curves." International Conference on Learning Representations, 2017.Markdown
[Pai et al. "Learning Invariant Representations of Planar Curves." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/pai2017iclr-learning/)BibTeX
@inproceedings{pai2017iclr-learning,
title = {{Learning Invariant Representations of Planar Curves}},
author = {Pai, Gautam and Wetzler, Aaron and Kimmel, Ron},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/pai2017iclr-learning/}
}