A novel curvature-based shape Fourier Descriptor

Abstract

Shape descriptors have demonstrated encouraging potential in retrieving images based on image content. A number of shape descriptors have been reported in the literature. Nevertheless, most of the reported descriptors still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on sound theoretical foundation, and possess computational efficiency and attractive invariance properties. In this paper, we propose a novel Fourier descriptor based on contour curvature. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as Curvature-Scale Image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the Curvature-Scale Image. This allows the descriptor to capture detailed dynamics of the shape curvature and enhance the efficiency of the shape matching process. Experiments using images from the MPEG-7 database have been conducted to compare the performance of the proposed descriptor with the Curvature- Scale-Space Descriptor (CSSD), the Generic Fourier Descriptor (GFD), and the 1-D Fourier Descriptor (1-FD). The proposed descriptor demonstrated superior performance.