Fully connected object proposals for video segmentation

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 3227-3234

  Perazzi, Federico ^a,b   Wang, Oliver ^b   Gross, Markus ^a,b   Sorkine Hornung, Alexander ^b  

^a ETH ZURICH   (Switzerland)

^b DISNEY RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

CONDITIONAL RANDOM FIELD; CONNECTED GRAPH; DISCRIMINATIVE POWER; ENERGY FUNCTIONS; MAXIMUM A POSTERIORI; MULTIPLE OBJECTS; SEGMENTATION ACCURACY; VIDEO SEGMENTATION;

IMAGE SEGMENTATION;

EID: 84973890095     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.369     Document Type: Conference Paper

References (44)

1. P. A. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marqués, and J. Malik. Multiscale combinatorial grouping. In Proc. CVPR, 2014.
2. D. Banica, A. Agape, A. Ion, and C. Sminchisescu. Video object segmentation by salient segment chain composition. In The IEEE International Conference on Computer Vision (ICCV) Workshops, December 2013.
3. S. Belongie, C. Fowlkes, F. R. K. Chung, and J. Malik. Spectral partitioning with indefinite kernels using the nyström extension. In Proc. ECCV, 2002.
4. W. Brendel and S. Todorovic. Video object segmentation by tracking regions. In Proc. ICCV, 2009.
5. T. Brox and J. Malik. Object segmentation by long term analysis of point trajectories. In Proc. ECCV, 2010.
6. S. Brutzer, B. Höferlin, and G. Heidemann. Evaluation of background subtraction techniques for video surveillance. In Proc. CVPR, 2011.
7. N. D. F. Campbell, K. Subr, and J. Kautz. Fully-connected crfs with non-parametric pairwise potential. In Proc. CVPR, 2013.
8. J. Carreira, F. Li, and C. Sminchisescu. Object recognition by sequential figure-ground ranking. IJCV, 98(3):243-262, 2012.
9. J. Carreira and C. Sminchisescu. CPMC: Automatic object segmentation using constrained parametric min-cuts. IEEE TPAMI, 34(7):1312-1328, 2012.
10. R. Chaudhry, A. Ravichandran, G. D. Hager, and R. Vidal. Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In Proc. CVPR, 2009.
11. M. Cheng, Z. Zhang, W. Lin, and P. H. S. Torr. BING: binarized normed gradients for objectness estimation at 300fps. In Proc. CVPR, 2014.
12. S.-C. S. Cheung and C. Kamath. Robust techniques for background subtraction in urban traffic video. In Visual Communications and Image Processing, volume 5308, pages 881-892, 2004.
13. T. Cox and M. Cox. Multidimensional Scaling. Chapman & Hall, London, 1994.
14. A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov. Bilayer segmentation of live video. In Proc. CVPR, 2006.
15. V. de Silva and J. B. Tenenbaum. Global versus local methods in nonlinear dimensionality reduction. In S. Becker, S. Thrun, and K. Obermayer, editors, Proc. NIPS, 2002.
16. I. Endres and D. Hoiem. Category-independent object proposals with diverse ranking. IEEE TPAMI, 36(2):222-234, 2014.
17. A. Faktor and M. Irani. Video segmentation by non-local consensus voting. In Proc. BMVC, 2014.
18. R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014.
19. M. Grundmann, V. Kwatra, M. Han, and I. A. Essa. Efficient hierarchical graph-based video segmentation. In Proc. CVPR, 2010.
20. E. Hayman and J. Eklundh. Statistical background subtraction for a mobile observer. In Proc. ICCV, 2003.
21. S. Hickson, S. Birchfield, I. A. Essa, and H. I. Christensen. Efficient hierarchical graph-based segmentation of RGBD videos. In Proc. CVPR, 2014.
22. M. Irani and P. Anandan. A unified approach to moving object detection in 2d and 3d scenes. IEEE TPAMI, 20(6):577-589, 1998.
23. P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with Gaussian edge potentials. In Proc. NIPS, 2011.
24. P. Krähenbühl and V. Koltun. Geodesic object proposals. In Proc. ECCV, 2014.
25. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
26. Y. J. Lee, J. Kim, and K. Grauman. Key-segments for video object segmentation. In Proc. ICCV, 2011.
27. F. Li, T. Kim, A. Humayun, D. Tsai, and J. M. Rehg. Video segmentation by tracking many figure-ground segments. In Proc. ICCV, 2013.
28. T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: common objects in context. In Proc. ECCV, 2014.
29. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
30. T. Ma and L. J. Latecki. Maximum weight cliques with mutex constraints for video object segmentation. In Proc. CVPR, 2012.
31. P. Ochs and T. Brox. Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions. In Proc. ICCV, 2011.
32. A. Papazoglou and V. Ferrari. Fast object segmentation in unconstrained video. In Proc. ICCV, 2013.
33. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
34. F. Perazzi, P. Krähenbühl, Y. Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. In Proc. CVPR, 2012.
35. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in large margin classifiers, pages 61-74, 1999.
36. J. C. Platt. Fastmap, metricmap, and landmark mds are all nystrom algorithms. In Proc. Workshop on Artificial Intelligence and Statistics, pages 261-268, 2005.
37. S. A. Ramakanth and R. V. Babu. Seamseg: Video object segmentation using patch seams. In Proc. CVPR, 2014.
38. Y. Ren, C. Chua, and Y. Ho. Statistical background modeling for non-stationary camera. Pattern Recognition Letters, 24(1-3):183-196, 2003.
39. C. Rother, V. Kolmogorov, and A. Blake. "grabcut": interactive foreground extraction using iterated graph cuts. ACM Trans. Graph., 23(3):309-314, 2004.
40. M. Rubinstein, A. Shamir, and S. Avidan. Improved seam carving for video retargeting. ACM Trans. Graph., 27(3), 2008.
41. D. Varas and F. Marqués. Region-based particle filter for video object segmentation. In Proc. CVPR, 2014.
42. B. X. Wang and N. Japkowicz. Boosting support vector machines for imbalanced data sets. In Proc. ISMIS, 2008.
43. C. Xu and J. J. Corso. Evaluation of super-voxel methods for early video processing. In Proc. CVPR, 2012.
44. D. Zhang, O. Javed, and M. Shah. Video object segmentation through spatially accurate and temporally dense extraction of primary object regions. In Proc. CVPR, 2013.