Learning Metrics for Shape Classification and Discrimination

We propose a family of shape metrics that generalize the classical Procrustes distance by attributing weights to general linear combinations of landmarks. We develop an algorithm to learn a metric that is optimally suited to a given shape classification problem. Shape discrimination experiments are carried out with phantom data, as well as landmark data representing the shape of the wing of different species of fruit flies.