Latent Topic Random Fields: Learning using a taxonomy of labels

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  He, Xuming ^a   Zemel, Richard S ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONTEXT REPRESENTATION; IMAGE FEATURES; IMAGE LABELING; IMAGE REGIONS; IMAGE-SPACE; INPUT DATA; LABELED IMAGES; NEW FORMS; OBJECT BOUNDARIES; RANDOM FIELDS; REAL-WORLD DATASETS; SEMANTIC HIERARCHIES;

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; EDUCATION; FEATURE EXTRACTION; IMAGE ENHANCEMENT; IMAGE PROCESSING; INFORMATION THEORY; LABELS; PATTERN RECOGNITION;

LABELING;

EID: 51949107877     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587362     Document Type: Conference Paper

References (21)

1. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, 2003.
2. P. Carbonetto, N. de Freitas, and K. Barnard. A statistical model for general contextual object recognition. In ECCV, 2004.
3. X. Feng, C. K.Williams, and S. Felderhof. Combining belief networks and neural networks for scene segmentation. IEEE Trans. PAMI, 24(4):467-483, 2002.
4. X. He, R. S. Zemel, and D. Ray. Learning and incorporating top-down cues in image segmentation. In ECCV, 2006.
5. D. Hoiem, A. A. Efros, and M. Hebert. Putting objects in perspective. In CVPR, 2006.
6. M. Kelm, C. Pal, and A. McCallum. Combining generative and discriminative methods for pixel classification with multi-conditional learning. In ICPR, 2006.
7. S. Kumar and M. Hebert. Discriminative random fields: A discriminative framework for contextual interaction in classification. In ICCV, 2003.
8. S. Kumar and M. Hebert. A hierarchical field framework for unified context-based classification. In ICCV, 2005.
9. J. A. Lasserre, C. M. Bishop, and T. P. Minka. Principled hybrids of generative and discriminative models. In CVPR, 2006.
10. K. Murphy, A. Torralba, andW. T. Freeman. Using the forest to see the trees: A graphical model relating features, objects and scenes. In NIPS, 2003.
11. J. C. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. In BMVC, 2006.
12. J. Shotton, J. M. Winn, C. Rother, and A. Criminisi. Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In ECCV, 2006.
13. J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman. Discovering object categories in image collections. In ICCV, 2005.
14. E. B. Sudderth, A. Torralba, W. T. Freeman, and A. S. Willsky. Learning hierarchical models of scenes, objects, and parts. In ICCV, 2005.
15. A. Torralba. Contextual priming for object detection. IJCV, 53:169-191, 2003.
16. A. Torralba, R. Fergus, andW. T. Freeman. Object and scene recognition in tiny images. J. Vis., 7:193-193, 2007.
17. A. Torralba, K. P. Murphy, and W. T. Freeman. Contextual models for object detection using boosted random fields. In NIPS, 2005.
18. J. Verbeek and B. Triggs. Region classification with markov field aspect models. In CVPR, 2007.
19. G. Wang, Y. Zhang, and L. Fei-Fei. Using dependent regions for object categorization in a generative framework. In CVPR, 2006.
20. X. Wang and E. Grimson. Spatial latent Dirichlet allocation. In NIPS, 2007.
21. J. Winn and J. Shotton. The layout consistent random field for recognizing and segmenting partially occluded objects. In CVPR, 2006.