Beyond Pairwise Shape Similarity Analysis

This paper considers two major applications of shape matching algorithms: (a) query-by-example, i. e. retrieving the most similar shapes from a database and (b) finding clusters of shapes, each represented by a single prototype. Our approach goes beyond pairwise shape similarity analysis by considering the underlying structure of the shape manifold, which is estimated from the shape similarity scores between all the shapes within a database. We propose a modified mutual kNN graph as the underlying representation and demonstrate its performance for the task of shape retrieval. We further describe an efficient, unsupervised clustering method which uses the modified mutual kNN graph for initialization. Experimental evaluation proves the applicability of our method, e. g. by achieving the highest ever reported retrieval score of 93.40% on the well known MPEG-7 database.