Learning to Combine Bottom-Up and Top-Down Segmentation

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Learning to Combine Bottom-Up and Top-Down Segmentation
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 feature induction algorithm for CRF, which allows us to efficiently search over thousands of candidate fragments. Whereas pure top...
Anat Levin, Yair Weiss
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2006
Where ECCV
Authors Anat Levin, Yair Weiss
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