A graph-theoretic approach to 3D shape classification

Shape classification is an intriguing and challenging problem that lies at the crossroads of computer vision, geometry processing and machine learning. In this paper, we introduce a graph-theoretic approach for 3D shape classification using graph regularized sparse coding in conjunction with the biharmonic distance map. Our unified framework exploits both sparsity and dependence among the features of shape descriptors in a bid to design robust shape signatures that are effective in discriminating between shapes from different classes. In an effort to coherently capture the similarity between feature descriptors, we use multiclass support vector machines for 3D shape classification on mid-level features that are learned via graph regularized sparse coding. Our experiments on two standard 3D shape benchmarks show that the proposed framework not only outperforms the state-of-the-art methods in classification accuracy, but also provides attractive scalability in terms of computational efficiency.