Graphics enhanced version of this site

Understanding Popout through Repulsion
S. Yu and J. Shi
Computer Vision and Pattern Recognition, December, 2001.

Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference

Adobe portable document format (pdf) [428 KB]

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.

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

S. Yu and J. Shi, "Understanding Popout through Repulsion," Computer Vision and Pattern Recognition, December, 2001.

@inproceedings{Yu_2001_3817,
   author = "Stella Yu and Jianbo Shi",
   title = "Understanding Popout through Repulsion",
   booktitle = "Computer Vision and Pattern Recognition",
   month = "December",
   year = "2001"
}