Probabilistic mixtures of differential profiles for shape recognition

We present a new shape descriptor, the differential profile, for shape representation and recognition, which is derived from differential and geometric quantities evaluated at points on a shape. We model differential profiles from a class of shapes as a finite mixture of Gaussians, and use an expectation-maximization (EM) procedure for class-wise model learning. Experiments on handwritten digit and 3D object datasets give promising results.