A new and simple shape descriptor based on a non-parametric multiscale model

In this paper, we present a new and robust shape descriptor, which can be efficiently used to quickly prune a search for similar shapes in a large image database. The proposed shape descriptor is based on a multiscale representation of the discrete set of points, sampled from the internal and external contour points of the query and the candidate shapes. In this approach, dissimilarity between two shapes is defined as the reconstruction error, of the candidate shape, made by using multiscale elements of contours extracted from the query shape. This dissimilarity measure allows to quickly produce an accurate shortlist of candidate matches, ranked from the most similar to the least similar one, suitable for a more careful and more time consuming matching algorithm. Experiments on the Snodgrass & Vanderwart database allows to attest the discriminating power of this measure and its robustness to possible distortions, warping and occlusion artifacts.