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壁画图像分类中的分组多实例学习方法

DOI: 10.11834/jig.20140508

Keywords: 壁画图像,图像分类,多实例学习,LatentSVM(支持向量机)

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Abstract:

目的针对壁画图像具有较大类内差异以及具有较强背景噪音的特点,提出一种分组多实例学习的策略,实现对不同年代风格的壁画图像分类。方法将样本空间划分为不同的子空间,每一个子空间中的所有训练样本训练分类器模型,测试阶段根据测试样本落到的子空间来选择不同的分类模型对测试样本进行分类。在各个子空间训练分类器时,将每一幅壁画图像样本看做多个实例的组成,采用多实例学习的方式来训练分类器。训练过程中,引入隐变量用于标识每一个实例,隐变量的存在使得分类器的优化问题不是一个凸问题,无法用梯度下降法去直接求解,采用迭代的方式训练LatentSVM作为每一个子空间的分类器。结果实验结果表明本文方法在壁画图像的分类上与传统方法相比提高了平均5%的精度。结论本文分组多实例学习的策略在壁画分类问题中能够较大程度地解决图像的类内差异以及背景噪音对分类结果造成的影响。

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