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Abstract
Principal component analysis (PCA), as a key component in statistical learning, has been adopted in a wide variety of applications in computer vision and machine learning. From a different angle, weakly supervised learning, more specifically multiple instance learning (MIL), allows fine-grained information to be exploited from coarsely-grained label information. In this paper, we propose an algorithm using the robust PCA (RPCA) [1] in a iterative way to perform simultaneous common object discovery and model learning under a one-class multiple instance learning setting. We show the advantage of our method on common object discovery and model learning, which needs no fine/coarse alignment in the input data; in addition, it achieves comparable results with standard two-class MIL learning algorithms but our method is learning from one-class data only.
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Authors and Affiliations
Huazhong University of Science and Technology, China
Xinggang Wang, Xiang Bai & Wenyu Liu
Visual Computing Group, Microsoft Research Asia, China
Zhengdong Zhang, Yi Ma & Zhuowen Tu
Lab of Neuro Imaging and Department of Computer Science, UCLA, USA
Zhuowen Tu
- Xinggang Wang
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- Zhengdong Zhang
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- Yi Ma
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- Xiang Bai
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- Wenyu Liu
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Editors and Affiliations
Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, Seoul, Korea
Kyoung Mu Lee
Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China
Yasuyuki Matsushita
School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA
James M. Rehg
Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China
Zhanyi Hu
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Wang, X., Zhang, Z., Ma, Y., Bai, X., Liu, W., Tu, Z. (2013). One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_19
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