Unsupervised learning of stochastic AND-OR templates for object modeling

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 648-655

  Si, Zhangzhang ^a   Zhu, Song Chun ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HIERARCHICAL COMPOSITION; IMAGE MODELS; OBJECT MODELING; REAL EXAMPLE; VISUAL OBJECTS;

IMAGE PROCESSING; LEARNING ALGORITHMS; UNSUPERVISED LEARNING;

STOCHASTIC MODELS;

EID: 84856626189     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2011.6130304     Document Type: Conference Paper

References (17)

1. W. Brendel and S. Todorovic. Learning spatiotemporal graphs of human activities. In ICCV, 2011.
2. H. Chen, Z. Xu, Z. Liu, and S.-C. Zhu. Composite templates for cloth modeling and sketching. In CVPR, 2006.
3. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. TPAMI, 23(6):681-685, 2001.
4. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. TPAMI, 2010.
5. R. Fergus, P. Perona, and A. Zisserman. Weakly supervised scale-invariant learning of models for visual recognition. IJCV, 71(3):273-303, March 2007.
6. S. Fidler and A. Leonardis. Towards scalable representations of object categories: Learning a hierarchy of parts. In CVPR, 2007.
7. A. Gupta, P. Srinivasan, J. Shi, and L. S. Davis. Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In CVPR, 2009.
8. G. E. Hinton, S. Osindero, and Y. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, July 2006.
9. Y. Jin and S. Geman. Context and hierarchy in a probabilistic image model. In CVPR, 2006.
10. S. D. Pietra, V. D. Pietra, and J. Lafferty. Inducing features of random fields. TPAMI, 1997.
11. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio. Object recognition with cortex-like mechanisms. TPAMI, 29:411-426, 2007.
12. E. B. Sudderth, A. B. Torralba, W. T. Freeman, and A. S. Willsky. Describing visual scenes using transformed objects and parts. IJCV, 77(1-3):291-330, 2008.
13. S. Todorovic and N. Ahuja. Unsupervised category modeling, recognition, and segmentation in images. TPAMI, 30(12):2158-2174, 2008.
14. Y. N. Wu, Z. Si, H. Gong, and S.-C. Zhu. Learning active basis model for object detection and recognition. IJCV, 2009.
15. L. Zhu, Y. Chen, and A. Yuille. Unsupervised learning of probabilistic grammar-markov models for object categories. TPAMI, 2009.
16. S. C. Zhu and D. Mumford. A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision, 2(4):259-362, 2006.
17. S.-C. Zhu, Y. N. Wu, and D. B. Mumford. Minimax entropy principle and its applications to texture modeling. Neural Computation, 9(8)(2):1627-1660, 1997.