  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.
