Hierarchical density-based clustering of shapes

The article describes a novel way of representing large databases of shapes. We propose a hierarchical clustering of a set of Fourier-transformed contours. The clustering analysis is density-based and is performed using topographic maps. We have tested the approach on a database of extracted contours of marine animals, generated by F. Mokhtarian et al. (1996). The resulting clusters group contours that show similar global shapes, which sometimes differ from those grouped by Mokhtarian et al., due to a difference in similarity criterion