Interpretable Image Clustering via Diffeomorphism-Aware K-Means
Computer Vision and Pattern Recognition
2020-12-18 v1 Group Theory
Abstract
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of -means applied in the image space while addressing its clustering performance issues. Specifically, we develop a measure of similarity between images and centroids that encompasses a general class of deformations: diffeomorphisms, rendering the clustering invariant to them. Our work leverages the Thin-Plate Spline interpolation technique to efficiently learn diffeomorphisms best characterizing the image manifolds. Extensive numerical simulations show that our approach competes with state-of-the-art methods on various datasets.
Cite
@article{arxiv.2012.09743,
title = {Interpretable Image Clustering via Diffeomorphism-Aware K-Means},
author = {Romain Cosentino and Randall Balestriero and Yanis Bahroun and Anirvan Sengupta and Richard Baraniuk and Behnaam Aazhang},
journal= {arXiv preprint arXiv:2012.09743},
year = {2020}
}