Pull the plug? Predicting if computers or humans should segment images

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

Volumn 2016-December, Issue , 2016, Pages 382-391

  Gurari, Danna ^a   Jain, Suyog Dutt ^a   Betke, Margrit ^a   Grauman, Kristen ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

BUDGET CONTROL; COMPUTER VISION; FLUORESCENCE MICROSCOPY; FORECASTING; PATTERN RECOGNITION;

AUTOMATED METHODS; COARSE SEGMENTATION; FLUORESCENCE MICROSCOPY IMAGES; FOREGROUND OBJECTS; HUMAN ANNOTATIONS; PHASE-CONTRAST MICROSCOPY; PRACTICAL SOLUTIONS; SEGMENTATION TOOL;

IMAGE SEGMENTATION;

EID: 84986296889     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.48     Document Type: Conference Paper

References (39)

1. S. Alpert, M. Galun, R. Basri, and A. Brandt. Image segmentation by probabilistic bottom-up aggregation and cue integration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, 2007.
2. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (5): 898-916, 2011.
3. P. Arbeláez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik. Multiscale combinatorial grouping. In IEEE Conference on Computer Vision and Pattern Recognition, pages 328-335, 2014.
4. D. Ballard. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13 (2): 111-122, 1981.
5. D. Batra, A. Kowdle, D. Parikh, J. Luo, and T. Chen. iCoseg: Interactive co-segmentation with intelligent scribble guidance. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3169-3176. IEEE, 2010.
6. O. Bernard, D. Friboulet, P. Thevenaz, and M. Unser. Variational b-spline level-set: A linear filtering approach for fast, deformable model evolution. IEEE Transactions on Image Processing, 18 (6): 1179-1191, 2009.
7. A. Biswas and D. Parikh. Simultaneous active learning of classifiers&attributes via relative feedback. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 644-651, 2013.
8. S. Branson, V. H. Grant, C. Wah, P. Perona, and S. Belongie. The ignorant led by the blind: A hybrid human-machine vision system for fine-grained categorization. In International Journal of Computer Vision, volume 108, pages 3-29, 2014.
9. A. Carlier, V. Charvillat, A. Salvador, X. G. i Nieto, and O. Marques. Click'n'Cut: Crowdsourced interactive segmentation with object candidates. In International ACM Workshop on Crowdsourcing for Multimedia, pages 53-56, 2014.
10. J. Carreira and C. Sminchisescu. Constrained parametric min-cuts for automatic object segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3241-3248, 2010.
11. V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours. IEEE Transactions on Image Processing, 22 (1): 61-79, 1997.
12. T. Chan and L. Vese. Active contours without edges. IEEE Transactions on Image Processing, 10 (2): 266-277, 2001.
13. D. R. Chittajallu, S. Florian, R. H. Kohler, Y. Iwamoto, J. D. Orth, R. Weissleder, G. Danuser, and T. J. Mitchison. In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy. Nature Methods, 12 (6): 577-585, 2015.
14. J. Cui, Q. Yang, F. Wen, Q. Wu, C. Zhang, L. V. Gool, and X. Tang. Transductive object cutout. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, 2008.
15. I. Endres and D. Hoiem. Category independent object proposals. In European Conference on Computer Vision (ECCV), pages 575-588, 2010.
16. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88 (2): 303-338, 2010.
17. D. R. Glenn, K. Lee, H. Park, R. Weissleder, A. Yacoby, M. D. Lukin, H. Lee, R. L. Walsworth, and C. B. Connolly. Single-cell magnetic imaging using a quantum diamond microscope. Nature Methods, pages 736-738, 2015.
18. L. Grady, M. P. Jolly, and A. Seitz. Segmentation from a box. In IEEE International Conference on Computer Vision (ICCV), pages 367-374, 2011.
19. V. Gulshan, C. Rother, A. Criminisi, A. Blake, and A. Zisserman. Geodesic star convexity for interactive image segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3129-3136, 2010.
20. D. Gurari, D. Theriault, M. Sameki, and M. Betke. How to use level set methods to accurately find boundaries of cells in biomedical images Evaluation of six methods paired with automated and crowdsourced initial contours. Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI): Interactive Medical Image Computation (IMIC) Workshop, page 9 pp., 2014.
21. D. Gurari, D. Theriault, M. Sameki, B. Isenberg, T. A. Pham, A. Purwada, P. Solski, M. Walker, C. Zhang, J. Y. Wong, and M. Betke. How to collect segmentations for biomedical images A benchmark evaluating the performance of experts, crowdsourced non-experts, and algorithms. IEEE Winter conference on Applications in Computer Vision (WACV), page 8 pp., 2015.
22. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software: An update. 11 (1): 10-18, 2009.
23. S. D. Jain and K. Grauman. Predicting sufficient annotation strength for interactive foreground segmentation. In IEEE International Conference on Computer Vision (ICCV), pages 1313-1320. IEEE, 2013.
24. T. Kohlberger, V. Singh, C. Alvino, C. Bahlmann, and L. Grady. Evaluating segmentation error without ground truth. Medical Image Computing and Computer Assisted Intervention (MICCAI), pages 528-536, 2012.
25. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS), pages 1097-1105, 2012.
26. S. Lankton and A. Tannenbaum. Localizing region-based active contours. IEEE Transactions on Image Processing, 17 (11): 2029-2039, 2008.
27. V. Lempitsky, P. Kohli, C. Rother, and T. Sharp. Image segmentation with a bounding box prior. In IEEE International Conference on Computer Vision (ICCV), pages 277-284, 2009.
28. C. Li, C. Y. Kao, J. C. Gore, and Z. Ding. Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 17 (10): 1940-1949, 2008.
29. H. Li, F. Meng, B. Luo, and S. Zhu. Repairing bad co-segmentation using its quality evaluation and segment propagation. IEEE Transactions on Image Processing, 23 (8): 3545-3559, 2014.
30. D. Liu, Y. Xiong, K. Pulli, and L. Shapiro. Estimating image segmentation difficulty. In Machine Learning and Data Mining in Pattern Recognition, pages 484-495, 2011.
31. T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H. Shum. Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (2): 353-367, 2011.
32. M. Maitra, R. K. Gupta, and M. Mukherjee. Detection and counting of red blood cells in blood cell images using hough transform. International Journal of Computer Applications, 53 (16), 2012.
33. 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 International Conference on Computer Vision (ICCV), volume 2, pages 416-423, 2001.
34. N. Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9 (1): 62-66, 1979.
35. C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 3 (309-314), 2004.
36. B. Settles. Active learning literature survey. Technical report, University of Wisconsin, Madison, 2010. 2
37. S. Vijayanarasimhan and K. Grauman. Cost-sensitive active visual category learning. In International Journal of Computer Vision, volume 91, pages 24-44, 2011.
38. C. Wah, S. Maji, and S. Belongie. Learning localized perceptual similarity metrics for interactive categorization. In IEEEWinter Conference on Applications in Computer Vision (WACV), pages 502-509, 2015.
39. J. Wu, Y. Zhao, J. Zhu, S. Luo, and Z. Tu. Milcut: A sweeping line multiple instance learning paradigm for interactive image segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 256-263, 2014.