Co-spectral for robust shape clustering

Shape clustering is a difficult visual task due to large intra-class variations and small inter-class variations induced by shape articulation, rotation, occlusion, etc. To tackle this problem, we attempt to leverage the complementary nature among features of different statistics (e.g., skeleton-based descriptors and contour-based descriptors) for robust clustering. In this paper, a similarity fusion framework based on spectral analysis is proposed. The proposed method, which we call co-spectral, is a spectral clustering algorithm. It has two inborn merits for shape clustering: (1) it can automatically make use of the complementarity of various shape similarities based on a co-training framework; (2) it does not require shape representations to be vectors. Co-spectral is evaluated on several popular shape benchmarks. The experimental results demonstrate that co-spectral outperforms the state-of-the-art algorithms by a large margin.