Optimizing expected intersection-over-union with candidate-constrained CRFs

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 1850-1858

  Ahmed, Faruk ^a   Tarlow, Dany ^b   Batra, Dhruv ^c  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b MICROSOFT RESEARCH   (United States)

^c VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONSTRAINED OPTIMIZATION;

EVALUATION FUNCTION; HIGH QUALITY; IMAGE SEGMENTATION SYSTEM; NEW APPROACHES;

IMAGE SEGMENTATION;

EID: 84973882952     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.215     Document Type: Conference Paper

References (26)

1. D. Batra. An Efficient Message-Passing Algorithm for the M-Best MAP Problem. In Uncertainty in Artificial Intelligence, 2012.
2. D. Batra, P. Yadollahpour, A. Guzman-Rivera, and G. Shakhnarovich. Diverse M-Best Solutions in Markov Random Fields. In ECCV, 2012.
3. J. Berger. Statistical decision theory and Bayesian analysis. Springer, New York, NY [u. A. ], 2. ed edition, 1985.
4. G. Bouchard and O. Zoeter. Split variational inference. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009, pages 57-64, 2009.
5. J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In ECCV, pages 430-443, 2012.
6. J. Carreira and C. Sminchisescu. Constrained parametric min-cuts for automatic object segmentation. In CVPR, 2010.
7. G. Csurka, D. Larlus, F. Perronnin, and F. Meylan. What is a good evaluation measure for semantic segmentation? In 24th British Machine Vision Conference (BMVC), Sept 2013.
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/?q=node/874.
9. A. C. Gallagher, D. Batra, and D. Parikh. Inference for order reduction in markov random fields. In CVPR, 2011.
10. K. Gimpel, D. Batra, C. Dyer, and G. Shakhnarovich. A systematic exploration of diversity in machine translation. In EMNLP, 2013.
11. R. Gupta, A. A. Diwan, and S. Sarawagi. Efficient inference with cardinality-based clique potentials. In Proceedings of the 24th international conference on Machine learning, pages 329-336. ACM, 2007.
12. T. Jaakkola and M. Jordan. Improving the mean field approximation via the use of mixture distributions. In M. Jordan, editor, Learning in Graphical Models, volume 89 of NATO ASI Series, pages 163-173. Springer Netherlands, 1998.
13. P. Krähenbühl and V. Koltun. Parameter learning and convergent inference for dense random fields. In Proceedings of the 30th International Conference on Machine Learning, 2013.
14. S. Kumar and W. Byrne. Minimum bayes-risk decoding for statistical machine translation. Johns Hopkins Univ. Baltimore MD, Center for Language and Speech Processing (CLSP), 2004.
15. S. Kumar, W. Macherey, C. Dyer, and F. Och. Efficient minimum error rate training and minimum bayes-risk decoding for translation hypergraphs and lattices. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, volume 1. Association for Computational Linguistics, 2009.
16. J. Marroquin, S. Mitter, and T. Poggio. Probabilistic solution of ill-posed problems in computational vision. Technical report, Cambridge, MA, USA, 1987.
17. T. Minka and J. Winn. Gates: A graphical notation for mixture models. In In Proceedings of NIPS, volume 21, pages 1073-1080, 2008.
18. S. Nowozin. Optimal decisions from probabilistic models: The intersection-over-union case. In CVPR, 2014.
19. P. Pletscher and P. Kohli. Learning low-order models for enforcing high-order statistics. AISTATS, 2012.
20. V. Premachandran, D. Tarlow, and D. Batra. Empirical Minimum Bayes Risk Prediction: How to extract an extra few% performance from vision models with just three more parameters. In CVPR, 2014.
21. D. Tarlow and R. Adams. Revisiting uncertainty in graph cut solutions. In CVPR, 2012.
22. D. Tarlow, K. Swersky, R. S. Zemel, R. P. Adams, and B. J. Frey. Fast exact inference for recursive cardinality models. In UAI, 2012.
23. D. Tarlow and R. S. Zemel. Structured output learning with high order loss functions. In AISTATS, 2012.
24. R. W. Tromble, S. Kumar, F. Och, and W. Macherey. Lattice minimum bayes-risk decoding for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2008.
25. A. Wald. Statistical decision functions. Ann. Math. Statist., 20(2):165-205, 06 1949.
26. J. S. Yedidia, W. T. Freeman, and Y. Weiss. Constructing free energy approximations and generalized belief propagation algorithms. IEEE Trans. on Information Theory, 51(7), July 2005.