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Immiscible Color Flows in Optimal Transport Networks for Image Classification

2022-05-04 12:41:36
Alessandro Lonardi, Diego Baptista, Caterina De Bacco
arXiv_CV
       
Abstract

In classification tasks, it is crucial to meaningfully exploit information contained in data. Here, we propose a physics-inspired dynamical system that adapts Optimal Transport principles to effectively leverage color distributions of images. Our dynamics regulates immiscible fluxes of colors traveling on a network built from images. Instead of aggregating colors together, it treats them as different commodities that interact with a shared capacity on edges. Our method outperforms competitor algorithms on image classification tasks in datasets where color information matters.

Abstract (translated)

URL

https://arxiv.org/abs/2205.02938

PDF

https://arxiv.org/pdf/2205.02938.pdf

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