| Abstract |
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Perceptual popout is defined by both feature similarity
and local feature contrast. We identify these two measures
with attraction and repulsion, and unify the dual processes
of association by attraction and segregation by repulsion
in a single grouping framework. We generalize normalized
cuts to multi-way partitioning with these dual measures. We
expand graph partitioning approaches to weight matrices
with negative entries, and provide a theoretical basis for
solution regularization in such algorithms. We show that attraction,
repulsion and regularization each contributes in a
unique way to popout. Their roles are demonstrated in various
salience detection and visual search scenarios. This
work opens up the possibilities of encoding negative correlations
in constraint satisfaction problems, where solutions
by simple and robust eigendecomposition become possible.
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 Additional Information
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Citation:
Stella X. Yu, Jianbo Shi,
"Understanding Popout through Repulsion,"
cvpr,
p. 752,
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 2,
2001
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