Unbiased active learning for image retrieval

In transductive active learning, after selecting the samples for labeling using existing sample selection strategy such as close-to-boundary, the constructed labeled set will be under a different distribution from the unlabeled set, which violates the i.i.d assumption of existing classifier. In this paper, by explicitly considering the distribution difference, we propose an algorithm called unbiased active learning. In such algorithm, the distribution difference, so-called sample selection bias, is not only considered into the classifier, but also incorporated into the sample selection process for introducing a better sample selection strategy. We apply the proposed method to image retrieval and the experimental results show that our unbiased active learning algorithm outperforms existing approaches.