A Riemannian Framework for Linear and Quadratic Discriminant Analysis on the Tangent Space of Shapes

TL;DR

This paper introduces a Riemannian framework for discriminant analysis of shapes, leveraging tangent space representations and Fourier basis projections to classify complex shape data effectively.

Contribution

It develops a novel Riemannian approach for shape classification using tangent space analysis and Fourier basis, extending to infinite-dimensional shape spaces.

Findings

Effective classification on synthetic and real shape data.

Robust shape analysis invariant to translation, scaling, and rotation.

Demonstrated applicability to medical and biological shape datasets.

Abstract

We present a Riemannian framework for linear and quadratic discriminant classification on the tangent plane of the shape space of curves. The shape space is infinite dimensional and is constructed out of square root velocity functions of curves. We introduce the idea of mean and covariance of shape-valued random variables and samples from a tangent space to the pre-shape space (invariant to translation and scaling) and then extend it to the full shape space (rotational invariance). The shape observations from the population are approximated by coefficients of a Fourier basis of the tangent space. The algorithms for linear and quadratic discriminant analysis are then defined using reduced dimensional features obtained by projecting the original shape observations on to the truncated Fourier basis. We show classification results on synthetic data and shapes of cortical sulci, corpus callosum curves, as well as facial midline curve profiles from patients with fetal alcohol syndrome (FAS).