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Grouping with Bias
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Stella X. Yu and Jianbo Shi
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Technical Report CMU-RI-TR-01-22, Robotics Institute, Carnegie Mellon University, July 2001
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Paper
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Abstract
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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 influence 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 effectiveness of this algorithm on image segmentation with priors and object detection with spatial attention.
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Keywords
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image segmentation, figure-ground, grouping, graph partitioning, bias, spatial attention
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