Harmony potentials for joint classification and segmentation

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

Volumn , Issue , 2010, Pages 3280-3287

  Gonfaus, Josep M ^a   Boix, Xavier ^a   Van De Weijer, Joost ^a   Bagdanov, Andrew D ^a   Serrat, Joan ^a   Gonzàlez, Jordi ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS LABELS; CONDITIONAL RANDOM FIELD; CONTEXTUAL INFORMATION; DATA SETS; DIFFERENT SCALE; MULTIPLE CLASS; MULTIPLE LABELS; OBJECT SEGMENTATION; OPTIMIZATION PROBLEMS; SAMPLING STRATEGIES; SIMPLIFIED MODELS;

IMAGE SEGMENTATION;

COMPUTER VISION;

EID: 77955998994     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540048     Document Type: Conference Paper

References (24)

1. Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. PAMI, 26(9), 2004.
2. J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation. In CVPR, 2010.
3. G. Csurka and F. Perronnin. A simple high performance approach to semantic segmentation. In BMVC, 2008.
4. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2009 (VOC2009) Results. http://www.pascal-network.org/challenges/VOC/voc2009/workshop/index.html.
5. B. Fulkerson, A. Vedaldi, and S. Soatto. Class segmentation and object localization with superpixel neighborhoods. In ICCV, 2009.
6. S. Gould, J. Rodgers, D. Cohen, G. Elidan, and D. Koller. Multi-class segmentation with relative location prior. IJCV, 2008.
7. J. Jiang and Z. Tu. Efficient scale space auto-context for image segmentation and labeling. In CVPR, 2009.
8. P. Kohli, L. Ladicky, and P. Torr. Robust higher order potentials for enforcing label consistency. In CVPR, 2008.
9. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? PAMI, 26(9), 2004.
10. M. P. Kumar, P. Torr, and A. Zisserman. Obj cut. In CVPR, 2005.
11. S. Kumar and M. Hebert. A hierarchical field framework for unified context-based classification. In ICCV, 2005.
12. L. Ladicky, C. Russell, P. Kohli, , and P. Torr. Associative hierarchical crfs for object class image segmentation. In ICCV, 2009.
13. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
14. V. Lempitsky, P. Kohli, C. Rother, , and T. Sharp. Image segmentation with a bounding box prior. In ICCV, 2009.
15. L.-J. Li, R. Socher, and L. Fei-Fei. Towards total scene understanding: Classification, annotation and segmentation in an automatic framework. In CVPR, 2009.
16. S. Maji, A. C. Berg, and J. Malik. Classification using intersection kernel support vector machines is efficient. In CVPR, 2008.
17. N. Plath, M. Toussaint, and S. Nakajima. Multi-class image segmentation using conditional random fields and global classification. In ICML, 2009.
18. F. Shahbaz Khan, J. van de Weijer, and M. Vanrell. Top-down color attention for object recognition. In ICCV, 2009.
19. J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, 2008.
20. J. Shotton, J.Winn, C. Rother, and A. Criminisi. Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. IJCV, 2009.
21. K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluating color descriptors for object and scene recognition. IEEE PAMI, 2010.
22. J. Verbeek and B. Triggs. Scene segmentation with crfs learned from partially labeled images. In NIPS, 2008.
23. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV, 73(2), 2007.
24. L. Zhu, Y. Chen, Y. Lin, C. Lin, and A. L. Yuille. Recursive segmentation and recognition templates for 2d parsing. In NIPS, 2008.