Mesh Segmentation Using Laplacian Eigenvectors and Gaussian Mixtures

Avinash Sharma 1 Radu Horaud 1 David Knossow 1 Etienne Von Lavante 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : 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.
Type de document :
Communication dans un congrès
Richard Souvenir. AAAI Fall Symposium on Manifold Learning and its Applications, Nov 2009, Arlington, VA, United States. AAAI Press, pp.50-56, 2009
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Soumis le : mercredi 13 janvier 2010 - 22:45:47
Dernière modification le : mercredi 11 avril 2018 - 01:59:37
Document(s) archivé(s) le : jeudi 18 octobre 2012 - 12:30:31

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Avinash Sharma, Radu Horaud, David Knossow, Etienne Von Lavante. Mesh Segmentation Using Laplacian Eigenvectors and Gaussian Mixtures. Richard Souvenir. AAAI Fall Symposium on Manifold Learning and its Applications, Nov 2009, Arlington, VA, United States. AAAI Press, pp.50-56, 2009. 〈inria-00446990〉

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