The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

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.

Keywords

image segmentation, figure-ground, grouping, graph partitioning, bias, spatial attention

Notes

Sponsor: DARPA

Grant ID: ONR N00014-00-1-0915 and NSF IRI-9817496

Text Reference

Stella Yu and Jianbo Shi, "Grouping with Bias," tech. report CMU-RI-TR-01-22, Robotics Institute, Carnegie Mellon University, July, 2001

BibTeX Reference

@techreport{Yu_2001_3765,
   author = "Stella Yu and Jianbo Shi",
   title = "Grouping with Bias",
   booktitle = "",
   institution = "Robotics Institute",
   month = "July",
   year = "2001",
   number= "CMU-RI-TR-01-22",
   address= "Pittsburgh, PA",
}

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