Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer
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.
Cite
Text
Sharma et al. "Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15555-0_54Markdown
[Sharma et al. "Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/sharma2010eccv-learning/) doi:10.1007/978-3-642-15555-0_54BibTeX
@inproceedings{sharma2010eccv-learning,
title = {{Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer}},
author = {Sharma, Avinash and von Lavante, Etienne and Horaud, Radu},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {743-756},
doi = {10.1007/978-3-642-15555-0_54},
url = {https://mlanthology.org/eccv/2010/sharma2010eccv-learning/}
}