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Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer

Avinash Sharma 1 Etienne von Lavante 1 Radu Horaud 1 
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a shape from a training data set, followed by a probabilistic label transfer algorithm that is used to match two shapes and to transfer cluster labels from a training-shape to a test-shape. The novelty resides both in the use of the Laplacian embedding to propagate must-link and cannot-link constraints, and in the segmentation algorithm which is based on a learn, align, transfer, and classify paradigm. We compare the results obtained with our method with other constrained spectral clustering methods and we assess its performance based on ground-truth data.
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Submitted on : Tuesday, December 21, 2010 - 9:23:42 PM
Last modification on : Thursday, January 20, 2022 - 5:28:07 PM
Long-term archiving on: : Tuesday, March 22, 2011 - 3:14:21 AM


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Avinash Sharma, Etienne von Lavante, Radu Horaud. Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer. ECCV 2010 - European Conference on Computer Vision, Sep 2010, Heraklion, Greece. pp.743-756, ⟨10.1007/978-3-642-15555-0_54⟩. ⟨inria-00549401⟩



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