Learning to combine bottom-up and top-down segmentation

  • Authors:

  • Anat Levin;Yair Weiss

  • Affiliations:

  • School of Computer Science and Engineering, The Hebrew University of Jerusalem;School of Computer Science and Engineering, The Hebrew University of Jerusalem

  • Venue:
  • ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
  • Year:
  • 2006

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Abstract

Bottom-up segmentation based only on low-level cues is a notoriously difficult problem. This difficulty has lead to recent top-down segmentation algorithms that are based on class-specific image information. Despite the success of top-down algorithms, they often give coarse segmentations that can be significantly refined using low-level cues. This raises the question of how to combine both top-down and bottom-up cues in a principled manner. In this paper we approach this problem using supervised learning. Given a training set of ground truth segmentations we train a fragment-based segmentation algorithm which takes into account both bottom-up and top-down cues simultaneously, in contrast to most existing algorithms which train top-down and bottom-up modules separately. We formulate the problem in the framework of Conditional Random Fields (CRF) and derive a novel feature induction algorithm for CRF, which allows us to efficiently search over thousands of candidate fragments. Whereas pure top-down algorithms often require hundreds of fragments, our simultaneous learning procedure yields algorithms with a handful of fragments that are combined with low-level cues to efficiently compute high quality segmentations.