Shape similarity measure based on correspondence of visual parts

A cognitively motivated similarity measure is presented and its properties are analyzed with respect to retrieval of similar objects in image database s of silhouettes of 2D objects. To reduce influence of digitization noise. as well as segmentation errors, the shapes are simplified by a novel proces s of digital curve evolution. To compute our similarity measure, we first e stablish the best possible correspondence of visual parts (without explicit ly computing the visual parts). Then, the similarity between corresponding parts is computed and aggregated. We applied our similarity measure to shap e 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 a nd is stable with respect to noise distortions.