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

  • Affiliations:

  • Huazhong University of Science and Technology, China;Visual Computing Group, Microsoft Research Asia, China;Visual Computing Group, Microsoft Research Asia, China;Huazhong University of Science and Technology, China;Huazhong University of Science and Technology, China;Visual Computing Group, Microsoft Research Asia, China,Lab of Neuro Imaging and Department of Computer Science, UCLA

  • Venue:
  • ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
  • Year:
  • 2012

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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.