Context-guided diffusion for label propagation on graphs

Kim, Kwang In and Tompkin, James and Pfister, Hanspeter and Theobalt, Christian (2015) Context-guided diffusion for label propagation on graphs. In: 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, pp. 2776-2784. ISBN 9781467383905

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Abstract

Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regularizer. Inspired by the success of diffusivity tensors for anisotropic diffusion in image processing, we presents anisotropic diffusion on graphs and the corresponding label propagation algorithm. We develop positive definite diffusivity operators on the vector bundles of Riemannian manifolds, and discretize them to diffusivity operators on graphs. This enables us to easily define new robust diffusivity operators which significantly improve semi-supervised learning performance over existing diffusion algorithms.

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ID Code:
76393
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Deposited On:
27 Oct 2015 15:28
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Dec 2020 07:30