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Abstract:
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.
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