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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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