Web: http://arxiv.org/abs/2205.02938

May 9, 2022, 1:10 a.m. | Alessandro Lonardi, Diego Baptista, Caterina De Bacco

cs.CV updates on arXiv.org arxiv.org

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.

arxiv classification color cv image networks transport

More from arxiv.org / cs.CV updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California