A principled deep random field model for image segmentation

Pushmeet Kohli, Anton Osokin, Stefanie Jegelka

Research output: Contribution to journalConference articlepeer-review

62 Scopus citations

Abstract

We discuss a model for image segmentation that is able to overcome the short-boundary bias observed in standard pairwise random field based approaches. To wit, we show that a random field with multi-layered hidden units can encode boundary preserving higher order potentials such as the ones used in the cooperative cuts model of [12] while still allowing for fast and exact MAP inference. Exact inference allows our model to outperform previous image segmentation methods, and to see the true effect of coupling graph edges. Finally, our model can be easily extended to handle segmentation instances with multiple labels, for which it yields promising results.

Original languageEnglish
Article number6619101
Pages (from-to)1971-1978
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
StatePublished - 2013
Externally publishedYes
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: 23 Jun 201328 Jun 2013

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