Boundary detection benchmarking: Beyond F-measures

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 2123-2130

  Hou, Xiaodi ^a   Yuille, Alan ^b   Koch, Christof ^a  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

benchmarking; Boundary detection; dataset bias

Indexed keywords

BOUNDARY DETECTION; DATASET BIAS; F-MEASURE; ILL POSED PROBLEM; LABELED DATASETS; PSYCHOPHYSICAL EXPERIMENTS;

ALGORITHMS; PATTERN RECOGNITION;

BENCHMARKING;

EID: 84887340408     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.276     Document Type: Conference Paper

References (27)

1. S. Alpert, M. Galun, R. Basri, and A. Brandt. Image segmentation by probabilistic bottom-up aggregation and cue integration. In CVPR 2007. IEEE Conference on, pages 1-8. IEEE, 2007. 1
2. P. Arbelaez. Boundary extraction in natural images using ultrametric contour maps. In CVPR Workshop, 2006. IEEE Conference on, pages 182-182. IEEE, 2006. 3
3. P. Arbeláez, B. Hariharan, C. Gu, S. Gupta, L. Bourdev, and J. Malik. Semantic segmentation using regions and parts. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3378-3385. IEEE, 2012. 6
4. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(5):898-916, 2011. 3, 7
5. E. Borenstein and S. Ullman. Class-specific, top-down segmentation. Computer Vision-ECCV 2002, pages 639-641, 2002. 1
6. T. Cour, F. Benezit, and J. Shi. Spectral segmentation with multiscale graph decomposition. In CVPR, 2005. IEEE Conference on, volume 2, pages 1124-1131. IEEE, 2005. 6
7. P. Dollar, Z. Tu, and S. Belongie. Supervised learning of edges and object boundaries. In CVPR, 2006 IEEE Conference on, volume 2, pages 1964-1971. IEEE, 2006. 3
8. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www. pascalnetwork. org/challenges/VOC/voc2012/workshop/index. html. 1
9. P. Felzenszwalb and D. McAllester. A min-cover approach for finding salient curves. In CVPR Workshop, 2006. IEEE Conference on, pages 185-185. IEEE, 2006. 3
10. V. Ferrari, T. Tuytelaars, and L. Van Gool. Object detection by contour segment networks. Computer Vision-ECCV 2006, pages 14-28, 2006. 6
11. A. Goldberg and R. Kennedy. An efficient cost scaling algorithm for the assignment problem. Mathematical Programming, 71(2):153-177, 1995. 2
12. R. Kennedy, J. Gallier, and J. Shi. Contour cut: identifying salient contours in images by solving a hermitian eigenvalue problem. In CVPR, 2011. IEEE Conference on, pages 2065-2072. IEEE, 2011. 3
13. I. Kokkinos. Boundary detection using f-measure-, filterand feature-(f 3) boost. Computer Vision-ECCV 2010, pages 650-663, 2010. 3
14. T. Leung and J. Malik. Contour continuity in region based image segmentation. Computer Vision-ECCV 1998, pages 544-559, 1998. 6
15. D. Martin, C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(5):530-549, 2004. 2, 3
16. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Computer Vision-ICCV 2001. IEEE International Conference on, volume 2, pages 416-423. IEEE, 2001. 1, 2, 3
17. D. Martin, J. Malik, and D. Patterson. An Empirical Approach to Grouping and Segmentaqtion. Computer Science Division, University of California, 2003. 2
18. V. Raykar, S. Yu, L. Zhao, A. Jerebko, C. Florin, G. Valadez, L. Bogoni, and L. Moy. Supervised learning from multiple experts: Whom to trust when everyone lies a bit. In ICML, 2009. ACM Conference on, pages 889-896. ACM, 2009. 3
19. X. Ren. Multi-scale improves boundary detection in natural images. Computer Vision-ECCV 2008, pages 533-545, 2008. 3
20. X. Ren and L. Bo. Discriminatively trained sparse code gradients for contour detection. Advances in Neural Information Processing Systems, 25, 2012. 2, 3
21. X. Ren, C. Fowlkes, and J. Malik. Figure/ground assignment in natural images. Computer Vision-ECCV 2006, pages 614-627, 2006. 6
22. A. Sorokin and D. Forsyth. Utility data annotation with amazon mechanical turk. In CVPR Workshops, 2008. IEEE Conference on, pages 1-8. IEEE, 2008. 3
23. A. Torralba and A. Efros. Unbiased look at dataset bias. In CVPR, 2011. IEEE Conference on, pages 1521-1528. IEEE, 2011. 3
24. S. Vittayakorn and J. Hays. Quality assessment for crowdsourced object annotations. In Proceedings of the British machine vision conference, pages 109-1, 2011. 3
25. S. Wang, T. Kubota, and J. Siskind. Salient boundary detection using ratio contour. Advances in Neural Information Processing Systems, 16, 2003. 3
26. P. Welinder, S. Branson, S. Belongie, and P. Perona. The multidimensional wisdom of crowds. In In Proc. of NIPS, pages 2424-2432, 2010. 3
27. Q. Zhu, G. Song, and J. Shi. Untangling cycles for contour grouping. In Computer Vision-ICCV 2007. IEEE Conference on, pages 1-8. IEEE, 2007. 3