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Low level vision via switchable Markov random fields

Dahua Lin,John W. Fisher III

Published 2012 in 2012 IEEE Conference on Computer Vision and Pattern Recognition

ABSTRACT

Markov random fields play a central role in solving a variety of low level vision problems, including denoising, in-painting, segmentation, and motion estimation. Much previous work was based on MRFs with hand-crafted networks, yet the underlying graphical structure is rarely explored. In this paper, we show that if appropriately estimated, the MRF's graphical structure, which captures significant information about appearance and motion, can provide crucial guidance to low level vision tasks. Motivated by this observation, we propose a principled framework to solve low level vision tasks via an exponential family of MRFs with variable structures, which we call Switchable MRFs. The approach explicitly seeks a structure that optimally adapts to the image or video along the pursuit of task-specific goals. Through theoretical analysis and experimental study, we demonstrate that the proposed method addresses a number of drawbacks suffered by previous methods, including failure to capture heavy-tail statistics, computational difficulties, and lack of generality.

PUBLICATION RECORD

  • Publication year

    2012

  • Venue

    2012 IEEE Conference on Computer Vision and Pattern Recognition

  • Publication date

    2012-06-01

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    DOI 10.1109/CVPR.2012.6247957
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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