Scale-Free Image Keypoints Using Differentiable Persistent Homology
Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno, Marco Guerra, Gabriele Berton, Carlo Masone
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.
BibTeX
@inproceedings{
barbarani2024scalefree,
title={Scale-Free Image Keypoints Using Differentiable Persistent Homology},
author={Giovanni Barbarani and Francesco Vaccarino and Gabriele Trivigno and Marco Guerra and Gabriele Berton and Carlo Masone},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=fNJbcxhxRj}
}