Immiscible Color Flows in Optimal Transport Networks for Image Classification
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.
Alessandro Lonardi
PhD student
The main focus of my current research is studying routing problems combining approaches stemming from optimal transport and belief propagation. In particular, I am interested in understanding how different route selection mechanisms affect traffic and total path length of networks. The applications of my work span from urban to biological networks. Previously I was a Master’s Student in Mathematical Engineering at UniPd (Padua, Italy), where I also obtained my Bachelor’s degree in Physics.
Diego Baptista Theuerkauf
PhD student
My research focuses on analising graph-based approximations of solutions of optimal transportation problems. We use biologically-inspired models to find transport plans for many different routing frameworks.
Caterina De Bacco
Associate Professor
My research focuses on understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems.