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Perceiving Systems Empirical Inference Conference Paper 2011

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

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Perceiving Systems
Peter Vincent Gehler
  • Research Group Leader
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Robust Machine Learning
Martin Kiefel
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Empirical Inference
Lumin Zhang
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Empirical Inference
Bernhard Schölkopf
  • Director
Problem

We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.

Author(s): Peter Gehler and Carsten Rother and Martin Kiefel and Lumin Zhang and Bernhard Schölkopf
Links:
Book Title: Advances in Neural Information Processing Systems 24
Pages: 765-773
Year: 2011
Editors: Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.
Publisher: Curran Associates, Inc.
Project(s):
Bibtex Type: Conference Paper (inproceedings)
Address: Red Hook, NY, USA
Event Name: Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
Event Place: Granada, Spain
Electronic Archiving: grant_archive
Attachments:

BibTex 

@inproceedings{gehler11nips,
  title = {Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance},
  booktitle = {Advances in Neural Information Processing Systems 24},
  abstract = {We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.},
  pages = {765-773},
  editors = {Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.},
  publisher = {Curran Associates, Inc.},
  address = {Red Hook, NY, USA},
  year = {2011},
  slug = {gehler11nips},
  author = {Gehler, Peter and Rother, Carsten and Kiefel, Martin and Zhang, Lumin and Sch{\"o}lkopf, Bernhard}
}