Classification of two-dimensional figures using skeleton-geodesic histograms of thicknesses and distances

The paper considers a problem of shape representation and classification. We propose a skeleton-geodesic histogram of thicknesses and distances for this purpose. It is based on the statistics of pair distances between shape elements. It is computed using skeleton-geodesic distances and thickness differences between pairs of skeleton edges. This differs from conventional geodesic histograms that are calculated for all figure points. The switch to the skeleton edges and areas of their attraction significantly speeds up the calculation of skeleton-geodesic histogram of thicknesses and distances, while maintaining many useful properties inherent in usual geodesic histograms. Extensive experimentation has been conducted on the most difficult binary shape database. Obtained classification results indicate the high potential of the proposed descriptor.