Interactive video segmentation using occlusion boundaries and temporally coherent superpixels

2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Volumn , Issue , 2014, Pages 784-791

  Dondera, Radu ^a   Morariu, Vlad ^a   Wang, Yulu ^a   Davis, Larry ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS;

ADDITIONAL EXPERIMENTS; FOREGROUND OBJECTS; INTERACTIVE VIDEO SEGMENTATIONS; SEGMENTATION RESULTS; SPATIO-TEMPORAL STRUCTURES; STATE-OF-THE-ART SYSTEM; UNSUPERVISED CLUSTERING; WEAKLY SUPERVISED LEARNING;

IMAGE SEGMENTATION;

EID: 84904632254     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2014.6836023     Document Type: Conference Paper

References (23)

1. V. Badrinarayanan, F. Galasso, and R. Cipolla. Label propagation in video sequences. In IEEE Conference on Computer Vision and Pattern Recognition, 2010
2. X. Bai, J. Wang, and G. Sapiro. Dynamic color flow: A motion-Adaptive color model for object segmentation in video. In European Conference on Computer Vision, 2010
3. X. Bai, J. Wang, D. Simons, and G. Sapiro. Video snapcut: Robust video object cutout using localized classifiers. In ACM Transactions on Graphics, 2009
4. T. Brox and J. Malik. Object segmentation by long term analysis of point trajectories. In European Conference on Computer Vision, 2010
5. I. Endres and D. Hoiem. Category independent object proposals. In European Conference on Computer Vision, 2010
6. P. Felzenszwalb and D. Huttenlocher. Efficient graph-based image segmentation. In International Journal of Computer Vision, 2004
7. F. Galasso, R. Cipolla, and B. Schiele. Video segmentation with superpixels. In Asian Conference on Computer Vision, 2012
8. M. Grundmann, V. Kwatra, M. Han, and I. Essa. Efficient hierarchical graph based video segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, 2010
9. S. Kamvar, D. Klein, and C. Manning. Spectral learning. In International Joint Conference On Artificial Intelligence, 2003
10. Y. Lee, J. Kim, and K. Grauman. Key-segments for video object segmentation. In IEEE International Conference on Computer Vision, 2011
11. A. Levinshtein, C. Sminchisescu, and S. Dickinson. Spatiotemporal closure. In Asian Conference on Computer Vision, 2010
12. Z. Lu and M. Carreira-Perpinan. Constrained spectral clustering through affinity propagation. In IEEE Conference on Computer Vision and Pattern Recognition, 2008
13. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Neural Information Processing Systems, 2001
14. B. Price, B. Morse, and S. Cohen. Livecut: Learningbased interactive video segmentation by evaluation of multiple propagated cues. In IEEE International Conference on Computer Vision, 2009
15. S. Rangapuram and M. Hein. Constrained 1-spectral clustering. In Journal of Machine Learning Research, 2012
16. A. Sharma, E. von Lavante, and R. Horaud. Learning shape segmentation using constrained spectral clustering and probabilistic label transfer. In European Conference on Computer Vision, 2010
17. D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. In IEEE Conference on Computer Vision and Pattern Recognition, 2010
18. N. Sundaram, T. Brox, and K. Keutzer. Dense point trajectories by gpu-Accelerated large displacement optical flow. In European Conference on Computer Vision, 2010
19. A. Vazquez-Reina, S. Avidan, H. Pfister, and E. Miller. Multiple hypothesis video segmentation from superpixel flows. In European Conference on Computer Vision, 2010
20. S. Vijayanarasimhan and K. Grauman. Active frame selection for label propagation in videos. In European Conference on Computer Vision, 2012
21. J. Wang, P. Bhat, R. Colburn, M. Agrawala, and M. Cohen. Interactive video cutout. In ACM Transactions on Graphics, 2005
22. X.Wang and I. Davidson. Flexible constrained spectral clustering. In International conference on knowledge discovery and data mining, 2010
23. L. Xu, W. Li, and D. Schuurmans. Fast normalized cut with linear constraints. In IEEE Conference on Computer Vision and Pattern Recognition, 2009.