Abstract
We present a novel local shape descriptor by means of General Adaptive Neighborhoods (GANs) based on the properties of the heat diffusion process on a Riemannian manifold. The GAN is a spatial region, surrounding the feature point and fitting its local shape structure, which is isometric. Our signature, called the Heat Propagation Contours (HPCs), is obtained by analysing the well-known heat kernel and extracting contours automatically within the GAN as heat dissipates from the feature point onto the rest of the shape. HPCs capture geometric information around the feature point by investigating the heat propagation process both in the temporal and spatial domain. HPCs share many useful characteristics with the heat based methods. Particularly, it captures the intrinsic geometry of a shape and is suitable for non-rigid shape analysis. In addition, our signature provides an elegant and efficient way to describe the neighborhood of the feature point in a multiscale approach. The proposed descriptor is evaluated on several datasets to demonstrate its effectiveness.