Interpretable Image Clustering via Diffeomorphism-Aware K-Means

• URL: http://arxiv.org/abs/2012.09743v1
• Date: Wed, 16 Dec 2020 16:11:39 GMT
• Title: Interpretable Image Clustering via Diffeomorphism-Aware K-Means
• Authors: Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan Sengupta, Richard Baraniuk, Behnaam Aazhang
• Abstract summary: We develop a measure of similarity between images and centroids that encompasses a general class of deformations: diffeomorphisms. We show that our approach competes with state-of-the-art methods on various datasets.
• Score: 20.747301413801843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of $K$-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.

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