Invariant curvature-based Fourier shape descriptors

Shape descriptors have demonstrated encouraging potential for retrieving images based on image content, and a number of them have been reported in the literature. Nevertheless, most of the reported descriptors are still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on a sound theoretical foundation and also have the advantages of computational efficiency and attractive invariance properties. This paper proposes a new curvature-based Fourier descriptor (CBFD) for shape retrieval. 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 method allows the descriptor to capture the detailed dynamics of the shape curvature and enhance the efficiency of the shape-matching process. Experiments using the widely known MPEG-7 databases in conjunction with a created noisy database have been conducted in order to compare the performance of the proposed descriptor with six commonly used shape-retrieval descriptors: curvature-scale-space descriptor (CSSD), angular radial transform descriptors (ARTD), Zernike moment descriptors (ZMD), radial Tchebichef moment descriptors (RTMD), generic Fourier descriptor (GFD), and the 1-D Fourier descriptor (1-FD). The performance of the proposed descriptor has surpassed that of many of these notable descriptors. (c) 2012 Elsevier Inc. All rights reserved.