Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 1657-1664

  Fang, Chen ^a   Xu, Ye ^a   Rockmore, Daniel N ^a  

^a Dartmouth College   (United States)

Author keywords

dataset bias; domain generalization; metric learning; web images

Indexed keywords

IMAGE CLASSIFICATION; SEARCH ENGINES;

CLASSIFICATION SYSTEM; DATASET BIAS; DOMAIN GENERALIZATION; ILLUMINATION CONDITIONS; LEARNING FRAMEWORKS; METRIC LEARNING; MULTIPLE DATA SETS; WEB IMAGES;

CLASSIFICATION (OF INFORMATION);

EID: 84898795612     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.208     Document Type: Conference Paper

References (27)

1. M. Benito, J. Parker, Q. Du, J. Wu, D. Xiang, C. M. Perou, and J. S. Marron. Adjustment of systematic microarray data biases. Bioinformatics, 20(1):105-114, 2004.
2. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn., 3(1):1-122, Jan. 2011.
3. M. J. Choi, J. J. Lim, A. Torralba, and A. S. Willsky. Exploiting hierarchical context on a large database of object categories. In CVPR, 2010.
4. M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman. The Pascal visual object classes (voc) challenge. IJCV, 88(2):303-338, June 2010.
5. T. Evgeniou and M. Pontil. Regularized multi-task learning. In ACM SIGKDD, pages 109-117, 2004.
6. C. Fang and L. Torresani. Measuring image distances via embedding in a semantic manifold. In ECCV, 2012.
7. L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst., 106(1):59-70, Apr. 2007.
8. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google's image search. In ICCV, 2005.
9. A. Globerson and S. Roweis. Metric learning by collapsing classes. NIPS, 2006.
10. L. jia Li, G.Wang, and L. Fei-fei. Optimol: automatic online picture collection via incremental model learning. In CVPR, 2007.
11. T. Joachims. A support vector method for multivariate performance measures. In ICML, 2005.
12. A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba. Undoing the damage of dataset bias. In ECCV, 2012.
13. D. Lim, B. Mcfee, and G. R. Lanckriet. Robust structural metric learning. In ICML, 2013.
14. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, Nov. 2004.
15. B. Mcfee and G. Lanckriet. Metric learning to rank. In ICML, 2010.
16. K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. IJCV, 60(1):63-86, 2004.
17. S. Parameswaran and K. Weinberger. Large margin multitask metric learning. In NIPS. 2010.
18. W. Ping, Y. Xu, J. Wang, and X.-S. Hua. Famer: Making multi-instance learning better and faster. In SDM, 2011.
19. M. Rastegari, C. Fang, and L. Torresani. Scalable objectclass retrieval with approximate and top-k ranking. In ICCV, 2011.
20. B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: A database and web-based tool for image annotation. IJCV, 77(1-3):157-173, May 2008.
21. K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, 2010.
22. T. Tommasi, N. Quadrianto, B. Caputo, and C. H. Lampert. Beyond dataset bias: Multi-task unaligned shared knowledge transfer. In ACCV, 2012.
23. A. Torralba and A. Efros. Unbiased look at dataset bias. In CVPR, 2011.
24. K. Q. Weinberger and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res., 10:207-244, June 2009.
25. Y. Xu, W. Ping, and A. Campbell. Multi-instance metric learning. In ICDM, 2011.
26. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009.
27. Y. Yue and T. Finley. A support vector method for optimizing average precision. In SIGIR, 2007.