Multiclass semantic video segmentation with object-level active inference

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

Volumn 07-12-June-2015, Issue , 2015, Pages 4286-4294

  Liu, Buyu ^a   He, Xuming ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; EFFICIENCY; INFERENCE ENGINES; MARKOV PROCESSES; PATTERN RECOGNITION; SEMANTICS;

ACCURACY IMPROVEMENT; DESIGNED MODELS; INFERENCE ALGORITHM; LONG-RANGE DEPENDENCIES; MARKOV DECISION PROCESSES; OPTIMAL POLICIES; SPATIO-TEMPORAL DOMAINS; VIDEO SEGMENTATION;

IMAGE SEGMENTATION;

EID: 84959243955     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299057     Document Type: Conference Paper

References (32)

1. P. Abbeel and A. Y. Ng. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning, page 1. ACM, 2004
2. G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla. Segmentation and recognition using structure from motion point clouds. In ECCV, 2008
3. J. Chang, D. Wei, and J. W. F. III. A Video Representation Using Temporal Superpixels. In CVPR, 2013
4. A. Y. C. Chen and J. J. Corso. Temporally consistent multiclass video-object segmentation with the video graph-shifts algorithm. In WMVC, 2011
5. S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In ICCV, 2009
6. S. Gould and X. He. Scene understanding by labeling pixels. Commun. ACM, 57(11):68-77, 2014
7. A. Grubb, D. Munoz, J. A. Bagnell, and M. Hebert. Speedmachines: Anytime structured prediction. arXiv preprint arXiv:1312. 0579, 2013
8. X. He, R. S. Zemel, and D. Ray. Learning and incorporating top-down cues in image segmentation. In Computer Vision-ECCV 2006, pages 338-351. Springer, 2006
9. P. Isola and C. Liu. Scene collaging: analysis and synthesis of natural images with semantic layers. In (ICCV), 2013 IEEE International Conference on, 2013
10. B. Kim, M. Sun, P. Kohli, and S. Savarese. Relating things and stuff by high-order potential modeling. In in ECCV'12 Workshop on Higher-Order Models and Global Constraints in Computer Vision, 2012
11. P. Kontschieder, P. Kohli, J. Shotton, and A. Criminisi. Geof: Geodesic forests for learning coupled predictors. In CVPR, 2013
12. P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with Gaussian edge potentials. In NIPS, 2011
13. A. Kundu, Y. Li, F. Dellaert, F. Li, and J. Rehg. Joint semantic segmentation and 3d reconstruction from monocular video. In ECCV, 2014
14. L. Ladick, P. Sturgess, K. Alahari, C. Russell, and P. H. S. Torr. What, where and how many? combining object detectors and crfs. In ECCV, 2010
15. M. G. Lagoudakis and R. Parr. Least-squares policy iteration. The Journal of Machine Learning Research, 4:1107-1149, 2003
16. J. Lezama, K. Alahari, J. Sivic, and I. Laptev. Track to the future: Spatio-temporal video segmentation with long-range motion cues. In CVPR, 2011
17. B. Liu, X. He, and S. Gould. Multi-class semantic video segmentation with exemplar-based object reasoning. In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, 2015
18. T. Malisiewicz, A. Gupta, and A. A. Efros. Ensemble of exemplar-svms for object detection and beyond. In ICCV, 2011
19. O. Miksik, D. Munoz, J. A. Bagnell, and M. Hebert. Efficient temporal consistency for streaming video scene analysis. In IEEE International Conference on Robotics and Automation (ICRA), 2013
20. H. Riemenschneider, A. Bódis-Szomorú, J. Weissenberg, and L. Van Gool. Learning where to classify in multi-view semantic segmentation. In Computer Vision-ECCV 2014, pages 516-532. Springer, 2014
21. G. Roig, X. Boix, R. D. Nijs, S. Ramos, K. Kuhnlenz, and L. V. Gool. Active MAP Inference in CRFs for Efficient Semantic Segmentation. In 2013 IEEE International Conference on Computer Vision, 2013
22. M. Sun, B.-s. Kim, P. Kohli, and S. Savarese. Relating things and stuff via object property interactions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(7), July 2014
23. C. A. Sutton and A. McCallum. Piecewise training for undirected models. CoRR, 2012
24. B. Taylor, A. Ayvaci, A. Ravichandran, and S. Soatto. Semantic video segmentation from occlusion relations within a convex optimization framework. In EMMCVPR, 2013
25. J. Tighe and S. Lazebnik. Superparsing-scalable nonparametric image parsing with superpixels. IJCV, 2013
26. J. Tighe and S. L. Marc Niethammer. Scene parsing with object instances and occlusion ordering. In CVPR, 2014
27. C. Wang, M. de La Gorce, and N. Paragios. Segmentation, ordering and multi-object tracking using graphical models. In ICCV, 2009
28. D. Weiss, B. Sapp, and B. Taskar. Dynamic structured model selection. In Computer Vision (ICCV), 2013
29. D. Weiss and B. Taskar. Learning Adaptive Value of Information for Structured Prediction. In Advances in Neural Information Processing (NIPS), 2013
30. C. Wojek, S. Roth, K. Schindler, and B. Schiele. Monocular 3d scene modeling and inference: Understanding multiobject traffic scenes. In ECCV, 2010
31. C. Wojek and B. Schiele. A dynamic conditional random field model for joint labeling of object and scene classes. In ECCV, 2008
32. J. Yao, S. Fidler, and R. Urtasun. Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation. In CVPR, 2012.