Relay Boost Fusion for Learning Rare Concepts in Multimedia

  • Dong Wang
  • Jianmin Li
  • Bo Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


This paper relates learning rare concepts for multimedia retrieval to a more general setting of imbalanced data. A Relay Boost (RL.Boost) algorithm is proposed to solve this imbalanced data problem by fusing multiple features extracted from the multimedia data. As a modified RankBoost algorithm, RL.Boost directly minimizes the ranking loss, rather than the classification error. RL.Boost also iteratively samples positive/negative pairs for a more balanced data set to get diverse weak ranking with different features, and combines them in a ranking ensemble. Experiments on the standard TRECVID 2005 benchmark data set show the effectiveness of the proposed algorithm.


Average Precision Ensemble Method Minority Class Weak Learner Positive Instance 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong Wang
    • 1
  • Jianmin Li
    • 1
  • Bo Zhang
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and System, Department of Computer Science and TechnologyTsinghua UniversityBeijingP.R. China

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