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Maximum classification optimization-based active learning for image classification

Traditional multi-class image classification needs a large number of training samples for building a classifier model. However, it is very time-consuming and costly to obtain labels for a large number of training samples from human experts. Active learning is a feasible solution. This paper proposes a maximum classification optimization method (MCO) for actively selecting unlabeled images to acquire labels. It integrated the information of an unlabeled sample from different perspectives with two steps. It first chooses a subset of candidates, and then selects the best from these candidates. Our experimental results show that the maximum classification optimization method outperforms two popular exiting methods (entropy-based uncertainty and BvSB).