Mesh Segmentation Using Laplacian Eigenvectors and Gaussian Mixtures

In this paper a new completely unsupervised mesh segmentation algorithm is proposed, which is based on the PCA interpretation of the Laplacian eigenvectors of the mesh and on parametric clustering using Gaussian mixtures. We analyse the geometric properties of these vectors and we devise a practical method that combines single-vector analysis with multiple-vector analysis. We attempt to characterize the projection of the graph onto each one of its eigenvectors based on PCA properties of the eigenvectors. We devise an unsupervised probabilistic method, based on one-dimensional Gaussian mixture modeling with model selection, to reveal the structure of each eigenvector. Based on this structure, we select a subset of eigenvectors among the set of the smallest non-null eigenvectors and we embed the mesh into the isometric space spanned by this selection of eigenvectors. The final clustering is performed via unsupervised classification based on learning a multi-dimensional Gaussian mixture model of the embedded graph.

@InProceedings\{SHKV09a,
  author       = "Sharma, Avinash and Horaud, Radu P. and Knossow, David and von Lavante, Etienne",
  title        = "Mesh Segmentation Using Laplacian Eigenvectors and Gaussian Mixtures",
  booktitle    = "Proceedings of AAAI Fall Symposium on Manifold Learning and its Applications",
  series       = "Fall Symposium Series Technical Reports",
  month        = "November",
  year         = "2009",
  publisher    = "AAAI Press",
  address      = "Arlington, VA",
  url          = "http://perception.inrialpes.fr/Publications/2009/SHKV09a"
}