Composite statistical inference for semantic segmentation

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

Volumn , Issue , 2013, Pages 3302-3309

  Li, Fuxin ^a   Carreira, Joao ^b   Lebanon, Guy ^a   Sminchisescu, Cristian ^c  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c LUND UNIVERSITY   (Sweden)

Author keywords

composite likelihood; composite statistical inference; recombination of segments; semantic segmentation

Indexed keywords

COMPLEX OBJECTS; COMPOSITE LIKELIHOOD; LATENT VARIABLE; OBJECT SEGMENTATION; RECOMBINATION OF SEGMENTS; SEMANTIC SEGMENTATION; STATISTICAL INFERENCE; STATISTICAL MODELING;

ALGORITHMS; INFERENCE ENGINES; PATTERN RECOGNITION; PIXELS; STATISTICAL METHODS;

IMAGE SEGMENTATION;

EID: 84887381281     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.424     Document Type: Conference Paper

References (21)

1. P. Arbelaez, B. Hariharan, C. Gu, S. Gupta, L. Bourdev, and J. Malik. Semantic segmentation using regions and parts. In CVPR, 2012. 1, 4
2. J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In ECCV, 2012. 7
3. J. Carreira, F. Li, and C. Sminchisescu. Object Recognition by Sequential Figure-Ground Ranking. IJCV, 2012. 7
4. J. Carreira and C. Sminchisescu. CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts. PAMI, 2012. 1, 4
5. C. Dann, P. V. Gehler, S. Roth, and S. Nowozin. Pottics the potts topic model for semantic image segmentation. In DAGM, 2012. 1
6. J. V. Dillon and G. Lebanon. Stochastic composite likelihood. J. Mach. Learn. Res., pages 2597-2633, 2010. 3
7. M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge 2012. www. pascalnetwork. org/challenges/VOC/voc2012/workshop/index. html. 7
8. J. Gonfaus, X. Boix, J. V. de Weijer, A. Bagdanov, J. Serrat, and J. Gonzlez. Harmony potentials for joint classification and segmentation. In CVPR, 2010. 1
9. S. Gould, T. Gao, and D. Koller. Region-based segmentation and object detection. In NIPS, 2009. 1
10. D. Hoiem, A. A. Efros, and M. Hebert. Recovering surface layout from an image. IJCV, 75(1):151-172, 2007. 1
11. A. Ion, J. Carreira, and C. Sminchisescu. Probabilistic join segmentation and labeling. In NIPS, 2011. 1, 7
12. P. Krahenbuhl and V. Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In NIPS, 2011. 1
13. M. P. Kumar, H. Turki, D. Preston, and D. Koller. Learning specificclass segmentation from diverse data. In ICCV, 2011. 1
14. L. Ladicky, C. Russell, P. Kohli, and P. Torr. Associative hierarchical crfs for object class image segmentation. In ICCV, 2009. 1
15. L. Ladicky, P. Sturgess, K. Alahari, C. Russell, and P. Torr. What,where and how many? combining object detectors and crfs. In ECCV, 2010. 1
16. F. Li, J. Carreira, G. Lebanon, and C. Sminchisescu. Composite statistical inference for semantic segmentation. Technical report, Georgia Institute of Technology, April 2013. 3, 5, 7
17. F. Li, J. Carreira, and C. Sminchisescu. Object recognition as ranking holistic figure-ground hypotheses. In CVPR, 2010. 1, 4, 7, 8
18. B. Lindsay. Composite likelihood methods. Contemporary Mathematics, 1988. 3
19. C. Pantofaru, C. Schmid, and M. Hebert. Object recognition by integrating multiple image segmentations. In ECCV, 2008. 1
20. C. Varin and P. Vidoni. A note on composite likelihood inference and model selection. Biometrika, 92, 2005. 3
21. W. Xia, Z. Song, J. Feng, L.-F. Cheong, and S. Yan. Segmentation over detection by coupled global and local sparse representations. In ECCV, 2012. 1