Graph-based semi-supervised learning with multiple labels

  • Authors:

  • Zheng-Jun Zha;Tao Mei;Jingdong Wang;Zengfu Wang;Xian-Sheng Hua

  • Affiliations:

  • MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Hefei 230027, PR China and Department of Automation, University of Science an ...;Internet Media Group, Microsoft Research Asia, Beijing 100190, PR China;Internet Media Group, Microsoft Research Asia, Beijing 100190, PR China;Department of Automation, University of Science and Technology of China, Hefei 230027, PR China;Internet Media Group, Microsoft Research Asia, Beijing 100190, PR China

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2009

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Abstract

Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simultaneously exploiting the inherent correlations among multiple labels and the label consistency over the graph. Based on the proposed framework, we further develop two novel graph-based algorithms. We apply the proposed methods to video concept detection over TRECVID 2006 corpus and report superior performance compared to the state-of-the-art graph-based approaches and the representative semi-supervised multi-label learning methods.