Shape Representation and Clustering Based on QuasiLaplace Spectrum

Shape representation and clustering based on spectral graph theory is a hot topic in the field of computer vision and pattern recognition. Aiming at the structure features of different shapes, the highdimensional data are obtained by means of singular value decomposition on quasiLaplace matrices of the skeleton of shapes. Furthermore, the shapes are clustered by analyzing the distribution of the projection in a lowdimensional space. The comparative experiments on the public dataset demonstrate the effectiveness of the proposed approach.