Grouping with Bias
نویسندگان
چکیده
We present a graph partitioning method to integrate prior knowledge in data grouping. We consider priors represented by three types of constraints: unitary constraints on labelling of groups, partial a priori grouping information, external in uence on binary constraints. They are modelled as biases in the grouping process. We incorporate these biases into graph partitioning criteria. Computationally this formulation leads to a constrained eigenproblem. We demonstrate the e ectiveness of this algorithm on image segmentation with priors and object detection with spatial attention.
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