Consensus strategy for clustering using RC-images

Contrary to most of the existing 3D shape clustering methods, in which all the objects in a dataset must be classified in clusters, in this paper we tackle an incomplete but reliable unsupervised clustering solution. The central idea lies in obtaining coherent 3D shape groups using a consensus between different similarity measures which are defined in a common 3D shape representation framework. Our goal, therefore, is to extract some consistent groups of objects, considering the incomplete classification, if this occurs, as a natural result. The Weighted Cone Curvature (WCC) is defined as an overall feature which synthesizes a set of curvature levels on the nodes of a standard triangular mesh representation. The WCC concept is used to define a master descriptor called an RC-Image on which up to eight similarity measures are defined. A hierarchical clustering process is then carried out for all the measures and evaluated by means of a clustering confidence measure. Finally, a consensus between the best measures is achieved to provide a coherent group of objects. The proposed clustering approach has been tested on a set of mesh models belonging to a wide variety of free-shape objects, yielding promising results. The results of our experiments demonstrate that both the 3D shape descriptor used and the clustering strategy proposed might be useful for future developments in the unsupervised grouping field.