Grouping with Bias - Robotics Institute Carnegie Mellon University

Grouping with Bias

Stella Yu and Jianbo Shi
Tech. Report, CMU-RI-TR-01-22, Robotics Institute, Carnegie Mellon University, July, 2001

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.

BibTeX

@techreport{Yu-2001-8270,
author = {Stella Yu and Jianbo Shi},
title = {Grouping with Bias},
year = {2001},
month = {July},
institute = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-01-22},
keywords = {image segmentation, figure-ground, grouping, graph partitioning, bias, spatial attention},
}