Median Shapes

TL;DR

This paper introduces a framework for defining, analyzing, and computing median shapes of finite sets of shapes represented as integral currents, including theoretical existence results and practical algorithms.

Contribution

It develops the concept of median shapes using integral currents, proves existence under regularization, and formulates the median shape problem as an integer linear program for efficient computation.

Findings

Median can be computed efficiently in practice.

Mass-regularized median always exists.

Median shapes can be non-smooth even with smooth inputs.

Abstract

We introduce and begin to explore the mean and median of finite sets of shapes represented as integral currents. The median can be computed efficiently in practice, and we focus most of our theoretical and computational attention on medians. We consider questions on the existence and regularity of medians. While the median might not exist in all cases, we show that a mass-regularized median is guaranteed to exist. When the input shapes are modeled by integral currents with shared boundaries in codimension $1$, we show that the median is guaranteed to exist, and is contained in the \emph{envelope} of the input currents. On the other hand, we show that medians can be \emph{wild} in this setting, and smooth inputs can generate non-smooth medians. For higher codimensions, we show that \emph{books} are minimizing for a finite set of $1$-currents in $\Bbb{R}^3$ with shared boundaries. As part of this proof, we present a new result in graph theory---that \emph{cozy} graphs are \emph{comfortable}---which should be of independent interest. Further, we show that regular points on the median have book-like tangent cones in this case. From the point of view of computation, we study the median shape in the settings of a finite simplicial complex. When the input shapes are represented by chains of the simplicial complex, we show that the problem of finding the median shape can be formulated as an integer linear program. This optimization problem can be solved as a linear program in practice, thus allowing one to compute median shapes efficiently. We provide open source code implementing our methods, which could also be used by anyone to experiment with ideas of their own. The software could be accessed at https://github.com/tbtraltaa/medianshape.