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Grouping with Bias
- Stella X. YU and Jianbo SHI
Conference of Neural Information Processing Systems (NIPS'01)
- Abstract
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With the optimization of pattern discrimination as a goal, graph
partitioning approaches often lack the capability to integrate prior
knowledge to guide grouping. In this paper, we consider priors from
unitary generative models, partially labeled data and spatial
attention. These priors are modelled as constraints in the solution
space. By imposing uniformity condition on the constraints, we
restrict the feasible space to one of smooth solutions. A subspace
projection method is developed to solve this constrained
eigenproblem. We demonstrate that simple priors can greatly improve
image segmentation results.
- Keywords
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image segmentation, figure-ground, grouping, graph partitioning, bias, spatial attention,
partially labeled data
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