Optimal decisions from probabilistic models: The intersection-over-union case

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

Volumn , Issue , 2014, Pages 548-555

  Nowozin, Sebastian ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

decision theory; structured prediction

Indexed keywords

APPROXIMATION ALGORITHMS; COMPUTER VISION; DECISION MAKING; IMAGE SEGMENTATION; LINEAR PROGRAMMING;

APPROXIMATE ALGORITHMS; BAYESIAN DECISION THEORY; OPTIMAL DECISION MAKING; PARAMETRIC LINEAR PROGRAMMING; PROBABILISTIC MODELING; PROBABILISTIC MODELS; STATISTICAL APPROXIMATION; STRUCTURED PREDICTION;

DECISION THEORY;

EID: 84911411712     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.77     Document Type: Conference Paper

References (50)

1. R. K. Ahuja, O. Ergun, J. B. Orlin, and A. P. Punnen. A survey of very large-scale neighborhood search techniques. In Proc. Workshop on Discrete Opt. Elsevier, 2002.
2. J. O. Berger. Statistical Decision Theory and Bayesian Analysis. Springer, 1985.
3. D. Bertsimas and J. N. Tsitsiklis. Introduction to Linear Optimization. Athena Scientific, 1997.
4. M. B. Blaschko and C. H. Lampert. Learning to localize objects with structured output regression. In ECCV, 2008.
5. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell, 23(11):1222-1239, 2001.
6. A. Cambini and L. Martein. Generalized Convexity and Optimization: Theory and Applications. Springer, 2008.
7. A. Charnes and W. W. Cooper. Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9:181-186, 1962.
8. V. Chvátal. Linear Programming. WH Freeman, 1983.
9. A. P. Dawid. The well-calibrated Bayesian. Journal of the American Statistical Association, 77:605-613, 1982.
10. J. Domke. Learning graphical model parameters with approximate marginal inference. IEEE Trans. Pattern Anal. Mach. Intell, 35(10):2454-2467, 2013.
11. M. Everingham, L. V. Gool, C. K. Williams, J. Winn, and A. Zisserman. The PASCAL voc2012 challenge results.
12. C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell, 35(8):1915-1929, 2013.
13. B. Fröhlich, E. Rodner, and J. Denzler. A fast approach for pixelwise labeling of facade images. In ICPR, 2010.
14. B. Fröhlich, E. Rodner, and J. Denzler. Semantic segmentation with millions of features: Integrating multiple cues in a combined random forest approach. In ACCV, 2012.
15. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell, 6(6):721-741, 1984.
16. S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In ICCV, 2009.
17. R. Heijmans. When does the expectation of a ratio equal the ratio of expectations? Statistical Papers, 40, 1999.
18. J. H. Kappes, B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, J. Lellmann, N. Komodakis, and C. Rother. A comparative study of modern inference techniques for discrete energy minimization problems. In CVPR. IEEE, 2013.
19. V. Kolmogorov, Y. Boykov, and C. Rother. Applications of parametric maxflow in computer vision. In ICCV, 2007.
20. H. Konno, Y. Yajima, and T. Matsui. Parametric simplex algorithm for solving a special class of nonconvex minimization problems. J. Glob. Opt., 1:65-81, 1991.
21. P. Krähenbühl and V. Koltun. Parameter learning and convergent inference for dense random fields. In ICML, 2013.
22. P. M. Kumar and D. Koller. Efficiently selecting regions for scene understanding. In CVPR, 2010.
23. D. Küttel and V. Ferrari. Learning to approximate global shape priors for figure-ground segmentation. In British Machine Vision Conference (BMVC), 2013.
24. V. S. Lempitsky, A. Vedaldi, and A. Zisserman. Pylon model for semantic segmentation. In NIPS, 2011.
25. J. L. Marroquin, S. K. Mitter, and T. A. Poggio. Probabilistic solutions of ill-posed problems in computational vision. J. of the Am. Stat. Assoc., 82(397):293, 1987.
26. D. McAllester. Generalization bounds and consistency for structured labeling. In Predicting Structured Data. MIT Press, 2007.
27. U. K. Müller. Risk of Bayesian inference in misspecified models, and the sandwich covariance matrix. Econometrica, 81(5):1805-1849, 2013.
28. D. Mumford and A. Desolneux. Pattern Theory: The Stochastic Analysis of Real-World Signals. CRC, 2010.
29. A. Niculescu-Mizil and R. Caruana. Predicting good probabilities with supervised learning. In ICML, 2005.
30. S. Nowozin and C. H. Lampert. Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision, 6(3-4):185-365, 2011.
31. S. Nowozin, C. Rother, S. Bagon, T. Sharp, B. Yao, and P. Kohli. Decision tree fields. In ICCV, 2011.
32. G. Parmigiani and L. Inoue. Decision Theory: Principles and Approaches. John Wiley and Sons, Ltd., 2009.
33. P. Pletscher and P. Kohli. Learning low-order models for enforcing high-order statistics. In AISTATS, 2012.
34. P. Pletscher, S. Nowozin, P. Kohli, and C. Rother. Putting MAP back on the map. In DAGM, 2011.
35. M. Ranjbar, G. Mori, and Y. Wang. Optimizing complex loss functions in structured prediction. In ECCV, 2010.
36. S. H. Rice. The expected value of the ratio of correlated random variables. (unpublished note).
37. S. H. Rice. A stochastic version of the price equation reveals the interplay of deterministic and stochastic processes in evolution. BMC Evolutionary Biology, 8(1), 2008.
38. S. Schaible and J. Shi. Fractional programming: the sum-of-ratios case. In M. Fukushima and Y. Yuan, editors, Proc. 2nd Japanese-Sino Opt. Meeting, pages 219-230, 2003.
39. H. Seltman. Approximations for mean and variance of a ratio. (unpublished note).
40. J. Shotton, A. W. Fitzgibbon, M. Cook, T. Sharp, M. Finoc-chio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from single depth images. In CVPR, 2011.
41. J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, 2008.
42. C. G. Small. Expansions and Asymptotics for Statistics. CRC Press, 2010.
43. D. Tarlow and R. P. Adams. Revisiting uncertainty in graph cut solutions. In CVPR, 2012.
44. D. Tarlow and R. S. Zemel. Big and tall: Large margin learning with high order loss. In CVPR Workshop on Inference in Graphical Models with Structured Potentials, 2011.
45. D. Tarlow and R. S. Zemel. Structured output learning with high order loss functions. In AISTATS, 2012.
46. M. Tawarmalani, S. Ahmed, and N. V. Sahinidis. Global optimization of 0-1 hyperbolic programs. Journal of Global Optimization, 24:385-416, 2002.
47. J. Tighe and S. Lazebnik. Superparsing-scalable nonpara-metric image parsing with superpixels. International Journal of Computer Vision, 101(2):329-349, 2013.
48. J. A. Tomlin. On pricing and backward transformation in linear programming. Math. Prog., 6:42-47, 1974.
49. Z. Tu, X. Chen, A. L. Yuille, and S. C. Zhu. Image parsing: Unifying segmentation, detection, and recognition. International Journal of Computer Vision, 63(2):113-140, 2005.
50. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 2nd edition, 2000.