Abstract

Due to the huge intra-class variations for visual concept detection, it is necessary for concept learning to collect large scale training data to cover a wide variety of samples as much as possible. But it presents great challenges on both how to collect and how to train the large scale data. In this paper, we propose a novel web image sampling approach and a novel group sparse ensemble learning approach to tackle these two challenging problems respectively. For data collection, in order to alleviate manual labeling efforts, we propose a web image sampling approach based on dictionary coherence to select coherent positive samples from web images. We propose to measure the coherence in terms of how dictionary atoms are shared because shared atoms represent common features with regard to a given concept and are robust to occlusion and corruption. For efficient training of large scale data, in order to exploit the hidden group structures of data, we propose a novel group sparse ensemble learning approach based on Automatic Group Sparse Coding (AutoGSC). After AutoGSC, we present an algorithm to use the reconstruction errors of data instances to calculate the ensemble gating function for ensemble construction and fusion. Experiments show that our proposed methods can achieve promising results and outperforms existing approaches.

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