An entropy-based approach for shape description

In this paper an automatic method for the selection of those Fourier descriptors which better correlate a 2D shape contour is presented. To this aim, shape description has been modeled as a non linear approximation problem and a strict relationship between transform entropy and the sorted version of the transformed analysed boundary is derived. As a result, Fourier descriptors are selected in a hierarchical way and the minimum number of coefficients able to give a nearly optimal shape boundary representation is automatically derived. The latter maximizes an entropic interpretation of a complexity-based similarity measure, i.e. the normalized information distance. Preliminary experimental results show that the proposed method is able to provide a compact and computationally effective description of shape boundary which guarantees a nearly optimal matching with the original one.