Learning from weakly supervised data by the expectation loss SVM (e-SVM) algorithm

Advances in Neural Information Processing Systems

Volumn 2, Issue January, 2014, Pages 1125-1133

  Zhu, Jun ^a   Mao, Junhua ^a   Yuille, Alan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER VISION; IMAGE SEGMENTATION; INFORMATION SCIENCE; OBJECT DETECTION; OBJECT RECOGNITION; SEMANTICS;

CLASSIFICATION TASKS; CONTINUOUS VALUE; DETECTION PERFORMANCE; SEMANTIC SEGMENTATION; STATE OF THE ART; TRAINING DATA; TRAINING EXAMPLE; TRAINING SAMPLE;

LEARNING ALGORITHMS;

EID: 84937851238     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. SLIC superpixels compared to state-of-the-art superpixel methods. TPAMI, 34(11): 2274-2282, 2012.
2. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In Advances in Neural Information Processing Systems 15, pages 561-568. MIT Press, 2003.
3. P. Arbelaez, B. Hariharan, C. Gu, S. Gupta, and J. Malik. Semantic segmentation using regions and parts. In CVPR, 2012.
4. J. Carreira and C. Sminchisescu. Cpmc: Automatic object segmentation using constrained parametric min-cuts. TPAMI, 34(7): 1312-1328, 2012.
5. J. a. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In ECCV, pages 430-443, 2012.
6. J. Deng, A. Berg, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results. http://www.image-net.org/challenges/LSVRC/2012/index.
7. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell., 89(1-2): 31-71, Jan. 1997.
8. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html.
9. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results. http://www.pascal-network.org/challenges/VOC/voc2011/workshop/index.html.
10. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 9: 1871-1874, 2008.
11. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. TPAMI, 32(9): 1627-1645, 2010.
12. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. IJCV, 59(2): 167-181, Sept. 2004.
13. S. Fidler, R. Mottaghi, A. L. Yuille, and R. Urtasun. Bottom-up segmentation for top-down detection. In CVPR, pages 3294-3301, 2013.
14. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
15. B. Hariharan, P. Arbelaez, R. Girshick, and J. Malik. Simultaneous detection and segmentation. In ECCV, 2014.
16. K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of ir techniques. TOIS, 20(4): 422-446, 2002.
17. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1106-1114, 2012.
18. M. Lapin, M. Hein, and B. Schiele. Learning using privileged information: Svm+ and weighted svm. Neural Networks, 53: 95-108, 2014.
19. F. Li, J. Carreira, and C. Sminchisescu. Object recognition as ranking holistic figure-ground hypotheses. In CVPR, pages 1712-1719, 2010.
20. Y. Liu, J. Liu, Z. Li, J. Tang, and H. Lu. Weakly-supervised dual clustering for image semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 2075-2082. IEEE, 2013.
21. A. Müller and S. Behnke. Multi-instance methods for partially supervised image segmentation. In PSL, pages 110-119, 2012.
22. X. Ren, L. Bo, and D. Fox. Rgb-(d) scene labeling: Features and algorithms. In CVPR, June 2012.
23. J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, pages 1-8, 2008.
24. J. Suykens, J. D. Brabanter, L. Lukas, and J. Vandewalle. Weighted least squares support vector machines: robustness and sparse approximation. NEUROCOMPUTING, 48: 85-105, 2002.
25. J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders. Selective search for object recognition. IJCV, 104(2): 154-171, 2013.
26. A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. TPAMI, 34(3): 480-492, 2012.
27. A. Vezhnevets, V. Ferrari, and J. Buhmann. Weakly supervised semantic segmentation with a multi image model. In ICCV, 2011.
28. X. Yang, Q. Song, and A. Cao. Weighted support vector machine for data classification. In IJCNN, 2005.