A distance-based shape descriptor invariant to similitude and its application to shape classification

International audiencePattern recognition usually requires the description or representation of shapes with some features, called shape descriptors. A shape descriptor generally needs to be invariant to some geometrical transformations (translation, rotation, scaling...). In addition, it has to be robust against slight deformations or noise damaging the shape. In this paper, a novel shape descriptor based on distances and invariant to similitude transformations is proposed. A dissimilarity measure associated to the proposed descriptor is then introduced to quantify the discrepancies between shapes. Performance tests were performed on the Kimia and MPEG7 image databases to evaluate the quality of the proposed descriptor. More specifically, the proposed method was evaluated for shape classification and showed better performance compared with some other methods from the literature