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Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer

  • Avinash Sharma
  • Etienne von Lavante
  • Radu Horaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

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.

Keywords

Spectral Cluster Cluster Label Pairwise Constraint Spectral Cluster Algorithm Weighted Adjacency Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

978-3-642-15555-0_54_MOESM1_ESM.wmv (3 mb)
Electronic Supplementary Material (3,100 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Avinash Sharma
    • 1
  • Etienne von Lavante
    • 1
  • Radu Horaud
    • 1
  1. 1.INRIA Grenoble Rhône-AlpesMontbonnotFrance

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