In this paper, we propose a novel image process scheme called class-based expansion learning for image classification, which aims at improving the supervision-stimulation frequency for the samples of the confusing classes. Class-based expansion learning takes a bottom-up growing strategy in a class-based expansion optimization fashion, which pays more attention to the quality of learning the fine-grained classification boundaries for the preferentially selected classes. Besides, we develop a class confusion criterion to select the confusing class preferentially for training. In this way, the classification boundaries of the confusing classes are frequently stimulated, resulting in a fine-grained form. Experimental results demonstrate the effectiveness of the proposed scheme on several benchmarks.

中文翻译：

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用于图像分类的渐进式基于类的扩展学习

在本文中，我们提出了一种新的图像处理方案，称为基于类的图像分类扩展学习，旨在提高混淆类样本的监督刺激频率。基于类的扩展学习在基于类的扩展优化方式中采用自下而上的增长策略，更注重学习优先选择的类的细粒度分类边界的质量。此外，我们开发了一个类混淆标准来优先选择混淆类进行训练。这样，混淆类的分类边界被频繁激发，从而形成细粒度的形式。实验结果证明了所提出的方案在几个基准上的有效性。