Unsupervised object class discovery via saliency-guided multiple class learning

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

Volumn , Issue , 2012, Pages 3218-3225

  Zhu, Jun Yan ^a,b   Wu, Jiajun ^a,b   Wei, Yichen ^a   Chang, Eric ^a   Tu, Zhuowen ^a,c  

^a MICROSOFT RESEARCH ASIA   (China)

^b TSINGHUA UNIVERSITY   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTEGRATED FRAMEWORKS; MULTI-CLASS; MULTIPLE CLASS; MULTIPLE INSTANCE LEARNING; OBJECT CLASS; SALIENCY DETECTION; TRAINING OBJECTS;

COMPUTER VISION; UNSUPERVISED LEARNING;

OBJECT RECOGNITION;

EID: 84866696900     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6248057     Document Type: Conference Paper

References (38)

1. B. Babenko, P. Dollár, Z. Tu, and S. Belongie. Simultaneous learning and alignment: Multi-instance and multi-pose learning. In ECCV workshop on Faces in Real-Life Images, 2008. 1, 3
2. D. Batra, A. Kowdle, D. Parikh, J. Luo, and T. Chen. icoseg: Interactive cosegmentation with intelligent scribble guidance. In CVPR, 2010. 6, 7
3. K. Chang, T. Liu, and S. Lai. From co-saliency to cosegmentation: An efficient and fully unsupervised energy minimization model. In CVPR, 2011. 2, 7
4. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu. Global contrast based salient region detection. In CVPR, 2011. 2, 3, 7
5. O. Chum and A. Zisserman. An exemplar model for learning object classes. In CVPR, 2007. 7, 8
6. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. J. Royal Statist. Soc. Series B, 39(1):1-38, 1977. 4
7. T. Deselaers, B. Alexe, and V. Ferrari. Localizing objects while learning their appearance. Technical report, ETH Zurich, 2010. 2, 7, 8
8. P. Dollár, B. Babenko, S. Belongie, P. Perona, and Z. Tu. Multiple component learning for object detection. In ECCV, 2008. 1, 3
9. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification (2nd Edition). Wiley-Interscience, 2 edition, Nov. 2001. 1, 2, 6
10. L. Fei-Fei, R. Fergus, S. Member, and P. Perona. One-shot learning of object categories. IEEE Trans. PAMI, 28(4), 2006. 1, 2, 6
11. P. F. Felzenszwalb, R. B. Girshick, and D. R. David A. McAllester. Object detection with discriminatively trained part-based models. IEEE Trans. PAMI, 32(9), 2010. 1
12. J. Feng, Y. Wei, L. Tao, C. Zhang, and J. Sun. Salient object detection by composition. In ICCV, 2011. 2, 3, 6, 7
13. V. Ferrari, T. Tuytelaars, and L. V. Gool. Object detection by contour segment networks. In ECCV, 2006. 1, 6
14. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. J. of Comp. and Sys. Sci., 55(1):119-139, 1997. 3
15. K. Grauman and T. Darrell. Unsupervised learning of categories from sets of partially matching image features. In CVPR, 2006. 1, 2, 6
16. X. Hou and L. Zhang. Saliency detection: A spectral residual approach. In CVPR, 2007. 2, 3
17. T. Jebara and A. Pentland. Maximum conditional likelihood via bound maximization and the cem algorithm. In NIPS, 1998. 5
18. G. Kim, C. Faloutsos, and M. Hebert. Unsupervised modeling of object categories using link analysis techniques. In CVPR, 2008. 1, 2, 6, 7
19. Y. J. Lee and K. Grauman. Foreground focus: Unsupervised learning from partially matching images. IJCV, 85:143-166, 2009. 1, 2, 6
20. Y. J. Lee and K. Grauman. Shape discovery from unlabeled image collections. In CVPR, 2009. 1, 2, 6
21. Y. J. Lee and K. Grauman. Learning the easy things first: Self-paced visual category discovery. In CVPR, 2011. 1, 2, 6
22. D. Liu and T. Chen. Unsupervised image categorization and object localization using topic models and correspondences between images. In ICCV, 2007. 1, 2, 6
23. N. Loeff and A. Farhadi. Scene discovery by matrix factorization. In ECCV, 2008. 1
24. L. Mason, J. Baxter, P. Bartlett, and M. Frean. Boosting algorithms as gradient descent. In NIPS, 2000. 3, 4, 5
25. R. Rahmani, S. A. Goldman, H. Zhang, J. Krettek, and J. E. Fritts. Localized content based image retrieval. IEEE Trans. PAMI, 30(11), 2008. 1, 2, 6, 7
26. B. C. Russell, A. A. Efros, J. Sivic, W. T. Freeman, and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. In CVPR, 2006. 1, 2, 7, 8
27. U. Rutishauser, D. Walther, C. Koch, and P. Perona. Is bottom-up attention useful for object recognition. In CVPR, 2004. 2, 6
28. J. Salojarvi, K. Puolamaki, and S. Kaski. Expectation maximization algorithms for conditional likelihoods. In NIPS, 2005. 5
29. S. Savarese and L. Fei-Fei. 3d generic object categorization, localization and pose estimation. In ICCV, 2007. 6
30. J. Shotton, A. W. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from single depth images. In CVPR, 2011. 1
31. J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, 2008. 1, 6
32. T. Tuytelaars, C. H. Lampert, M. B. Blaschko, and W. Buntine. Unsupervised object discovery: A comparison. IJCV, 2009. 1, 2
33. P. A. Viola and M. J. Jones. Robust real-time face detection. IJCV, 57(2), 2004. 1
34. P. A. Viola, J. C. Platt, and C. Zhang. Multiple instance boosting for object detection. In NIPS, 2006. 1, 2, 3, 4, 5
35. L. Xu, J. Neufeld, B. Larson, and D. Schuurmans. Maximum margin clustering. In NIPS, 2005. 1, 2, 3
36. D. Zhang, F. Wang, L. Si, and T. Li. Maximum margin multiple instance clustering. In IJCAI, 2009. 1, 2, 3, 6, 7
37. M.-L. Zhang and Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 31:47-68, August 2009. 1, 2, 3, 6, 7
38. L. Zhu, Y. Chen, and A. L. Yuille. Unsupervised learning of probabilistic grammar-markov models for object categories. IEEE Trans. PAMI, 31(10), 2009. 1, 2, 6