Discriminating Deformable Shape Classes

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

Bibtex Metadata Paper


Salvador Ruiz-correa, Linda Shapiro, Marina Meila, Gabriel Berson


We present and empirically test a novel approach for categorizing 3-D free form ob- ject 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 symbolic-signature representation to classify deformable shapes [10]. Our approach con- structs 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 large scale dis- crimination experiments on two well-defined classes that share many common distinctive features. The experimental results suggest that our method outperforms traditional numeric signature-based methodologies. 1