Scale-Free Image Keypoints Using Differentiable Persistent Homology
Abstract
In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scale dependency, and lack flexibility. This paper introduces a novel approach that leverages Morse theory and persistent homology, powerful tools rooted in algebraic topology. We propose a novel loss function based on the recent introduction of a notion of subgradient in persistent homology, paving the way towards topological learning. Our detector, MorseDet, is the first topology-based learning model for feature detection, which achieves competitive performance in keypoint repeatability and introduces a principled and theoretically robust approach to the problem.
Cite
Text
Barbarani et al. "Scale-Free Image Keypoints Using Differentiable Persistent Homology." International Conference on Machine Learning, 2024.Markdown
[Barbarani et al. "Scale-Free Image Keypoints Using Differentiable Persistent Homology." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/barbarani2024icml-scalefree/)BibTeX
@inproceedings{barbarani2024icml-scalefree,
title = {{Scale-Free Image Keypoints Using Differentiable Persistent Homology}},
author = {Barbarani, Giovanni and Vaccarino, Francesco and Trivigno, Gabriele and Guerra, Marco and Berton, Gabriele and Masone, Carlo},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {2990-3002},
volume = {235},
url = {https://mlanthology.org/icml/2024/barbarani2024icml-scalefree/}
}