RI Homepage Carnegie Mellon Homepage RI Homepage

The Robotics Institute

Carnegie Mellon Robotics Institute

Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary Detection

Andrew Stein, Thomas Stepleton, and Martial Hebert
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.


Download
  • Adobe portable document format (pdf) (3MB)
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.

Abstract
We propose a novel step toward the unsupervised segmentation of whole objects by combining "hints" of partial scene segmentation offered by multiple soft, binary mattes. These mattes are implied by a set of hypothesized object boundary fragments in the scene. Rather than trying to find or define a single "best" segmentation, we generate multiple segmentations of an image. This reflects contemporary methods for unsupervised object discovery from groups of images, and it allows us to define intuitive evaluation metrics for our sets of segmentations based on the accurate and parsimonious delineation of scene objects. Our proposed approach builds on recent advances in spectral clustering, image matting, and boundary detection. It is demonstrated qualitatively and quantitatively on a dataset of scenes and is suitable for current work in unsupervised object discovery without top-down knowledge.

Keywords
segmentation, boundary detection, matting

Notes

Text Reference
Andrew Stein, Thomas Stepleton, and Martial Hebert, "Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary Detection," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.

BibTeX Reference
@inproceedings{Stein_2008_6024,
   author = "Andrew Stein and Thomas Stepleton and Martial Hebert",
   title = "Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary Detection",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2008",
}

The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.
Contact Us | Update Instructions