Extracting Understandable 3D Object Groups with Multiple Similarity Metrics

Some of the main difficulties involved in the clustering problem are the interpretation of the clusters and the choice of the number of clusters. The imposition of a complete clustering, in which all the objects must be classified might lead to incoherent and not convincing groups. In this paper we present an approach which alleviates this problem by proposing incomplete but reliable clustering strategies. The method is based on two pillars: using a set of different metrics which are evaluated through a

clustering confidence measure

and achieving a hard/soft clustering consensus. This method is particularly addressed to 3D shape grouping in which the objects are represented through geometric features defined over mesh models. Our approach has been tested using eight metrics defined on geometrical descriptors in a collection of freeshape objects. The results show that in all cases the algorithm yields coherent and meaningful groups for several numbers of clusters. The clustering strategy here proposed might be useful for future developments in the unsupervised grouping field.