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Published in:
2013 | OriginalPaper | Chapter
One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery
Authors : Xinggang Wang, Zhengdong Zhang, Yi Ma, Xiang Bai, Wenyu Liu, Zhuowen Tu
Published in: Computer Vision – ACCV 2012
Publisher: Springer Berlin Heidelberg
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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.