An empirical study of object category recognition: Sequential testing with generalized samples

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2007, Pages

  Lin, Liang ^a,b   Peng, Shaowu ^d   Porway, Jake ^c   Zhu, Song Chun ^b,c   Wang, Yongtian ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b Lotus Hill Research Institute   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER NETWORKS; COMPUTER VISION;

EMPIRICAL STUDIES; INTERNATIONAL CONFERENCES; OBJECT CATEGORY; SEQUENTIAL TESTING;

OBJECT RECOGNITION;

EID: 50649086781     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4408873     Document Type: Conference Paper

References (22)

1. J. Porway, B. Yao, and S.C. Zhu, Learning An And-Or graph for Modeling and Recognizing Object Categories, Technical Report, Department of Statistics, UCLA, 2007.
2. B. Yao, X. Yang, and S.C. Zhu. Introduction to A Large Scale General Purpose Groundtruth Dataset: Methodology, Annotation tool, and Benchmarks, EMMCVPR, 2007.
3. C. E. Guo, S. C. Zhu, and Y. N. Wu. A Mathematical Theory of Primal Sketch and Sketchability, ICCV, 2003.
4. D. Crandall, P. Felzenszwalb, and D. Huttenlocher. Spatial Priors for Part-Based Recognition Using Statistical Models, CVPR, 2005.
5. F. Han and S.C. Zhu. Bottom-up/Top-down Image Parsing by Attribute Graph Grammar, ICCV, 2005.
6. F. Jurie and B. Triggs. Creating Efficient Codebooks for Visual Recognition, ICCV, 2005.
7. I. Ulusoy, C. Bishop. Generative Versus Discriminative Methods for Object Recognition, CVPR, 2005.
8. L. Fei-Fei and P. Perona. A Bayesian Hierarchical Model for Learning Natural Scene Categories, CVPR, 2005 2
9. H. Chen, Z. Xu, Z. Liu, and S.C. Zhu. Composite Templates for Cloth Modeling and Sketching, CVPR, 2006.
10. L. Lin, S.C. Zhu, and Y. Wang. Layered Graph Match with Graph Editing, CVPR, 2007.
11. M. P. Kumar, P. H. S. Torr, and A. Zisserman, Extending Pictorial Structures for Object Recognition, BMVC, 2004.
12. P. Dollar, Z. Tu, and S. Belongie. Supervised Learning of Edges and Object Boundaries, ICCV, 2005.
13. P. Felzenszwalb and D. Hut tenlocher. Pictorial Structures for Object Recognition, IJCV 61(1):55-79, 2005.
14. P. Viola and M. Jones. Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001.
15. R. Fergus, P. Perona, and A. Zisserman. Object Class Recognition by Unsupervised Scale-invariant Learning, CVPR, 2003.
16. R. Maree, P. Geurts, J. Piater, L. Wehenkel. Random Subwindows for Robust Image Classification, CVPR, 2005.
17. S. Ali and M. Shah. An Integrated Approach for Generic Object Detection Using Kernel PCA and Boosting, ICME, 2005.
18. S.C. Zhu and D. Mumford. A Stochastic Grammar of Images, Foundations and Trends in Computer Graphics and Vision, 2007(To appear). 2
19. S. Ullman, E. Sali, and M. Vidal-Naquet. A Fragment-Based Approach to Object Representation and Classification, Proc. 4th Intl Workshop on Visual Form, Capri, Italy, 2001.
20. S. Belongie, J. Malik, and J. Puzicha. Shape Matching and Object Recognition Using Shape Contexts. PAMI, 24(4):509-522, 2002.
21. T.F. Wu, G.S. Xia, and S.C. Zhu. Compositional Boosting for Computing Hierarchical Image Structures, CVPR, 2007.
22. Z. Tu: Probabilistic Boosting Tree. Learning Discriminative Models for Classification, Recognition, and Clustering, ICCV, 2005.