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Context-guided Diffusion for Label Propagation on Graphs

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1602.06439.pdf
(Preprint), 204KB

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Citation

Kim, K. I., Tompkin, J., Pfister, H., & Theobalt, C. (2016). Context-guided Diffusion for Label Propagation on Graphs. Retrieved from http://arxiv.org/abs/1602.06439.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-9A84-9
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.