Learning shape segmentation using constrained spectral clustering and probabilistic label transfer

Authors:
Avinash Sharma;Etienne von Lavante;Radu Horaud
Affiliations:
INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin, France;INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin, France;INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin, France
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Year:
2010

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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 cannotlink 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.