Abstract
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.
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Wang, D., Li, J., Zhang, B. (2006). Relay Boost Fusion for Learning Rare Concepts in Multimedia. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_28
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DOI: https://doi.org/10.1007/11788034_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36018-6
Online ISBN: 978-3-540-36019-3
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