Discriminating Deformable Shape Classes
Salvador Ruíz-Correa, Linda G. Shapiro, Marina Meilă, Gabriel Berson · 2003
We present and empirically test a novel approach for categorizing 3-D free form object shapes represented by range data. In contrast to traditional surface-signature based systems that use alignment to match specific objects, we adapted the newly introduced symbolicsignature representation to classify deformable shapes [12]. Our approach constructs an abstract description of shape classes using an ensemble of classifiers that learn object class parts and their corresponding geometrical relationships from a set of numeric and symbolic descriptors. We used our classification engine in a series of discrimination experiments on two well-defined classes that share many common distinctive features. The experimental results suggest that our method is, to the best of our knowledge, the first capable of classifying shapes that are difficult to discriminate by human standards. 1