Interactive object detection

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

Volumn , Issue , 2012, Pages 3242-3249

  Yao, Angela ^a   Gall, Juergen ^a,b   Leistner, Christian ^a   Van Gool, Luc ^a,c  

^a ETH ZURICH   (Switzerland)

^b MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^c UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ANNOTATION METHODS; COST MODELS; DATA SETS; DETECTING OBJECTS; DIGITAL IMAGE; HUMAN ANNOTATIONS; IMAGE ANNOTATION; INTERACTIVE OBJECTS; LEARNING PERFORMANCE; OBJECT DETECTORS; ON THE FLIES; SINGLE USERS; STILL IMAGES; USER STUDY; VIDEO ANNOTATIONS; VIDEO DATA; WAITING-TIME;

ELECTRICAL ENGINEERING; PATTERN RECOGNITION; SOFTWARE ENGINEERING;

COMPUTER VISION;

EID: 84866664389     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6248060     Document Type: Conference Paper

References (24)

1. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
2. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
3. T. Deselaers, B. Alexe, and V. Ferrari. Localizing objects while learning their appearance. In ECCV, 2010.
4. R. Fergus, Y.Weiss, and A. Torralba. Semi-supervised learning in gigantic image collections. In NIPS, 2009.
5. V. Ferrari, M. Marin-Jimenez, and A. Zisserman. Progressive search space reduction for human pose estimation. In CVPR, 2008.
6. J. Ferryman and A. Shahrokni. Pets2009: Dataset and challenge. In IEEE Int. W. on Performance Evaluation of Tracking and Surveillance, 2009.
7. J. Gall, A. Yao, N. Razavi, L. van Gool, and V. Lempitsky. Hough forests for object detection, tracking, and action recognition. PAMI, 33(11):2188-2202, 2011.
8. P. Jain and A. Kapoor. Active learning for large multi-class problems. In CVPR, 2009.
9. A. Joshi, F. Porikli, and N. Papanikolopoulos. Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback. In CVPR, 2010.
10. C. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2010.
11. Y. Lee and K. Grauman. Learning the easy things first: Selfpaced visual category discovery. In CVPR, 2011.
12. D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In SIGIR, 1994.
13. S. Oh et al. A large-scale benchmark dataset for event recognition in surveillance video. In CVPR, 2011.
14. D. Parikh and K. Grauman. Relative attributes. In ICCV, 2011.
15. N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In ICML, 2001.
16. B. Russell, A. Torralba, K. Murphy, and W. Freeman. Labelme: a database and web-based tool for image annotation. IJCV, 77(1-3), 2008.
17. S. Schulter, C. Leistner, P. Roth, H. Bischof, and L. van Gool. On-line hough forests. In BMVC, 2011.
18. B. Settles. From theories to queries: Active learning in practice. In Active Learning and Experimental Design W., pages 1-18. 2011.
19. A. Sorokin and D. Forsyth. Utility data annotation with amazon mechanical turk. In W. on Internet Vision, 2008.
20. S. Vijayanarasimhan and K. Grauman. Cost-sensitive active visual category learning. IJCV, 91(1), 2011.
21. S. Vijayanarasimhan and K. Grauman. Large-scale live active learning: Training object detectors with crawled data and crowds. In CVPR, 2011.
22. C. Vondrick and D. Ramanan. Video annotation and tracking with active learning. In NIPS, 2011.
23. C. Vondrick, D. Ramanan, and D. Patterson. Efficient video annotation with crowdsourced markets. In ECCV, 2010.
24. P. Welinder and P. Perona. Online crowdsourcing: rating annotators and obtaining cost-effective labels. In W. on Advancing Computer Vision with Humans in the Loop, 2010.