Learning collections of part models for object recognition

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 939-946

  Endres, Ian ^a   Shih, Kevin J ^a   Jiaa, Johnston ^a   Hoiem, Derek ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOOSTED CLASSIFIERS; BOUNDING BOX; DETECTION SYSTEM; KEYPOINTS; OBJECT CATEGORY DETECTION; WEAK LEARNER;

OBJECT RECOGNITION;

DETECTORS;

EID: 84887348680     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.126     Document Type: Conference Paper

References (22)

1. L. Bourdev. Poselets and their applications in high-level computer vision. http://www. cs. berkeley. edu/lbourdev/poselets/.
2. L. Bourdev, S. Maji, T. Brox, and J. Malik. Detecting people using mutually consistent poselet activations. In ECCV, 2010.
3. L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3d human pose annotations. In ICCV, 2009.
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
5. S. K. Divvala, A. A. Efros, and M. Hebert. How important are deformable parts in the deformable parts model? In Parts and Attributes Workshop, ECCV, 2012.
6. S. K. Divvala, A. A. Efros, and M. Hebert. Object instance sharing by enhanced bounding box correspondence. In BMVC, 2012.
7. I. Endres and D. Hoiem. Category independent object proposals. In ECCV, 2010.
8. I. Endres, V. Srikumar, M.-W. Chang, and D. Hoiem. Learning shared body plans. In CVPR, 2012.
9. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2010 Results. http://www. pascalnetwork. org/challenges/VOC/voc2010/workshop/index. html.
10. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. PAMI, 2010.
11. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28(2), 2000.
12. B. Hariharan, J. Malik, and D. Ramanan. Discriminative decorrelation for clustering and classification. In ECCV, 2012.
13. D. Hoiem, Y. Chodpathumwan, and Q. Dai. Diagnosing error in object detectors. In ECCV, 2012.
14. P. Kumar, B. Packer, and D. Koller. Self-paced learning for latent variable models. In NIPS, 2010.
15. T. Malisiewicz, A. Gupta, and A. A. Efros. Ensemble of exemplar-svms for object detection and beyond. In ICCV, 2011.
16. A. Opelt, A. Pinz, and A. Zisserman. Incremental learning of object detectors using a visual shape alphabet. In CVPR, 2006.
17. P. Ott and M. Everingham. Shared parts for deformable partbased models. In CVPR, 2011.
18. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, pages 61-74. MIT Press, 2000.
19. S. Singh, A. Gupta, and A. A. Efros. Unsupervised discovery of mid-level discriminative patches. In ECCV, 2012.
20. A. Torralba, K. P. Murphy, and W. T. Freeman. Sharing features: efficient boosting procedures for multiclass object detection. In CVPR. 2004.
21. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Multiple kernels for object detection. In ICCV, 2009.
22. X. Zhu, C. Vondrick, D. Ramanan, and C. Fowlkes. Do we need more training data or better models for object detection? In BMVC, 2012.