Weakly supervised learning of part-based spatial models for visual object recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3951 LNCS, Issue , 2006, Pages 16-29

  Crandall, David J ^a   Huttenlocher, Daniel P ^a  

^a Department of Obstetrics Gynecology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; FEATURE EXTRACTION; LEARNING SYSTEMS; MATHEMATICAL MODELS;

FEATURE DETECTOR; OBJECT LOCATION; RECOGNITION PERFORMANCE; SPATIAL MODELS;

OBJECT RECOGNITION;

EID: 33745831956     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11744023_2     Document Type: Conference Paper

References (14)

1. Y. Amit and A. Trouve. Pop: Patchwork of parts models for object recognition. Technical report, The University of Chicago, April 2005.
2. D.J. Crandall, P.P. Felzenszwalb, and D.P. Huttenlocher. Spatial priors for part-based recognition using statistical models. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10-17, 2005.
3. C. Dance, J. Willamowski, L. Fan, C. Bray, and G. Csurka. Visual categorization with bags of keypoints. In European Conference on Computer Vision, 2004.
4. Gyuri Dorko and Cordelia Schmid. Object class recognition using discriminative local features. Technical report, INRIA Grenoble, September 2005.
5. P.P. Felzenszwalb and D.P. Huttenlocher. Efficient matching of pictorial structures. In IEEE Confer-ence on Computer Vision and Pattern Recognition, pages 11:66-73, 2000.
6. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In IEEE Conference on Computer Vision and Pattern Recognition, 2003.
7. R. Fergus, P. Perona, and A. Zisserman. A sparse object category model for efficient learning and exhaustive recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 380-387, 2005.
8. S. Ioffe and D.A. Forsyth. Probabilistic methods for finding people. International Journal of Computer Vision, 43(1):45-68, 2001.
9. A. Opelt, M. Fussenegger, A. Pinz, and P. Auer. Weak hypotheses and boosting for generic object detection and recognition. In European Conference on Computer Vision, pages 71-84, 2004.
10. E. Sali S. Ullman and M. Vidal-Naquet. A fragment-based approach to object representation and classification. In 4th International Workshop on Visual Form, IWVF4, 2001.
11. H. Schneiderman and T. Kanade. Probabilistic formulation for object recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 1998.
12. T. Serre, L. Wolf, and T. Poggio. A new biologically motivated framework for robust object recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2005.
13. J. Winn, A. Criminisi, and T. Minka. Object categorization by learned universal visual dictionary. In IEEE International Conference on Computer Vision, 2005.
14. W. Zhang, B. Yu, D. Samaras, and G. Zelinsky. Object class recognition using multiple layer boosting with heterogeneous features. In IEEE Conference on Computer Vision and Pattern Recognition, 2005.