Learning global and reconfigurable part-based models for object detection

Proceedings - IEEE International Conference on Multimedia and Expo

Volumn , Issue , 2012, Pages 13-18

  Song, Xi ^a,b   Wu, Tianfu ^b   Xie, Yi ^a,b   Jia, Yunde ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b Lotus Hill Research Institute   (China)

Author keywords

Deformable Part based Model; Dynamic Programming; Latent SVM; Part Configuration

Indexed keywords

CLASSIFICATION ERROR RATE; CONFIGURATION SPACE; CYCLIC GRAPH; DATA SETS; FIGURES OF MERITS; HIDDEN VARIABLE; IMPROVING PERFORMANCE; LATENT SVM; METHOD OF LEARNING; OBJECT CATEGORIES; OBJECT DETECTION; OPTIMAL CONFIGURATIONS; PART CONFIGURATION; RECTANGULAR SHAPES; ROOT NODES; STATE-OF-THE-ART METHODS; TERMINAL NODES;

ASPECT RATIO; DEFORMATION; DIRECTED GRAPHS; DYNAMIC PROGRAMMING; EXHIBITIONS; STATISTICAL TESTS;

OBJECT RECOGNITION;

EID: 84868126895     PISSN: 19457871     EISSN: 1945788X     Source Type: Conference Proceeding    
DOI: 10.1109/ICME.2012.32     Document Type: Conference Paper

References (12)

1. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models," PAMI, vol. 32, no. 9, pp. 1627-1645, 2010.
2. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The pascal visual object classes (voc) challenge," IJCV, vol. 88, no. 2, pp. 303-338, 2010.
3. L. Zhu, Y. Chen, A. Yuille, and W. Freeman, "Latent hierarchical structural learning for object detection," in CVPR, 2010, pp. 1062-1069.
4. P. Felzenszwalb, R. Girshick, and D. McAllester, "Discriminatively trained deformable part models, release 4," http://people.cs.uchicago.edu/ pff/latent-release4/.
5. S. Geman, D. Potter, and Z. Y. Chi, "Composition systems," Quart. Appl. Math, vol. 60, no. 4, pp. 707-736, 2002.
6. S.-C. Zhu and D. Mumford, "A stochastic grammar of images," Found. Trends. Comput. Graph. Vis., vol. 2, no. 4, pp. 259-362, 2006.
7. M. Fischler and R. Elschlager, "The representation and matching of pictorial structures," IEEE Trans. Comput., vol. 22, pp. 67-92, 1973.
8. P. Felzenszwalb and D. Huttenlocher, "Pictorial structures for object recognition," IJCV, vol. 61, no. 1, pp. 55-79, 2005.
9. Y. N. Wu, Z. Z. Si, H. F. Gong, and S.-C. Zhu, "Learning active basis model for object detection and recognition," IJCV, vol. 90, no. 2, pp. 198-235, 2010.
10. J. Zhu, T. Wu, S.-C. Zhu, X. Yang, and W. Zhang, "Learning reconfigurable scene representation by tangram model," in WACV, 2012.
11. P. Felzenszwalb and D. McAllester, "Object detection grammars," University of Chicago, Computer Science TR-2010-02, Tech. Rep., 2010.
12. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in CVPR, 2005, pp. 886-893.