Group sparse ensemble learning for visual concept detection

Abstract

To exploit the hidden group structures of data and thus detect concepts in videos, this paper proposes a novel group sparse ensemble learning approach based on Automatic Group Sparse Coding (AutoGSC). We first adopt AutoGSC to learn both a common dictionary over different data groups and an individual group-specific dictionary for each data group which can help us to capture the discrimination information contained in different data groups. Next, we represent each data instance by using a sparse linear combination of both dictionaries. Finally, we propose an algorithm to use the reconstruction errors of data instances to calculate the ensemble gating function for ensemble construction and fusion. Experiments on the TRECVid 2008 benchmark show that the ensemble learning proposal achieves promising results and outperforms existing approaches. © Springer International Publishing Switzerland 2013.