Shape similarity measure for image database of occluding contours

A similarity measure for silhouettes of 2D objects is presented, and its properties are analyzed with respect to retrieval of similar objects in an image database. Our measure profits from a novel approach to subdivision of objects into parts of visual form. To compute our similarity measure, we first establish the best possible correspondence of visual parts, which is based on a correspondence of convex boundary arcs. Then the similarity between corresponding arcs is computed and aggregated. We applied our similarity measure to shape matching of object contours in various image databases and compared it to well-known approaches in the literature. The experimental results justify that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.