A Unified Approach for Detection, Classification and Segmentation

Derek Hoiem, Santosh Kumar Divvala, James H. Hays, Alexei A. Efros, and Martial Hebert
European Conference on Computer Vision (ECCV) 2008, PASCAL VOC 2008 Workshop, October, 2008.


Download
  • Adobe portable document format (pdf) (2MB)
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
To tackle the challenging dataset presented in PASCAL VOC 2008 challenge, we use a highly successful appearance-based detector and augment it with rich contextual cues extracted from the image to further improve its performance. Specifically, we train detectors to obtain the confidence that a window contains an object based solely on global scene statistics, nearby regions, the object position and size, geographic context and boundaries. Our interest is to study how much each of these contextual cues can add to the performance of the local appearance based detector.

Notes

Text Reference
Derek Hoiem, Santosh Kumar Divvala, James H. Hays, Alexei A. Efros, and Martial Hebert, "A Unified Approach for Detection, Classification and Segmentation," European Conference on Computer Vision (ECCV) 2008, PASCAL VOC 2008 Workshop, October, 2008.

BibTeX Reference
@inproceedings{Hoiem_2008_6227,
   author = "Derek Hoiem and Santosh Kumar Divvala and James H. Hays and Alexei A. Efros and Martial Hebert",
   title = "A Unified Approach for Detection, Classification and Segmentation",
   booktitle = "European Conference on Computer Vision (ECCV) 2008, PASCAL VOC 2008 Workshop",
   month = "October",
   year = "2008",
}