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Dictionary ensemble based multi instance active learning method for image categorization

机译:基于字典集成的多实例主动学习图像分类方法

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Multiple Instance Learning (MIL) has become a prominent framework for image categorization problem. In MIL framework, images are described as the combination of multiple regions. Active learning in MIL framework becomes useful when large amount of unlabeled data is available and labeling is too costly to handle for each unlabeled data. In the literature, there are some researches on MI active learning but none of them take advantage of the ensemble techniques and sparse coding. In this work, we study a Dictionary Ensemble based MI Active Learning method. Experiments show that the proposed algorithm has higher classification accuracy over other techniques.
机译:多实例学习(MIL)已成为图像分类问题的重要框架。在MIL框架中,图像被描述为多个区域的组合。当有大量未标记的数据可用并且标记成本太高而无法处理每个未标记的数据时,MIL框架中的主动学习变得很有用。在文献中,有一些关于MI主动学习的研究,但是都没有利用集成技术和稀疏编码的优势。在这项工作中,我们研究了一种基于字典集合的MI主动学习方法。实验表明,与其他技术相比,该算法具有更高的分类精度。

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