Asymmetric propagation based batch mode active learning for image retrieval

Relevance feedback is an effective approach to improve the performance of image retrieval by leveraging the labeling of human. In order to alleviate the burden of labeling, active learning method has been introduced to select the most informative samples for labeling. In this paper, we present a novel batch mode active learning scheme for informative sample selection. Inspired by the method of graph propagation, we not only take the correlation between labeled samples and unlabeled samples, but the correlation among unlabeled samples taken into account as well. Especially, considering the unbalanced distribution of samples and the personalized feedback of human we propose an asymmetric propagation scheme to unify the various criteria including uncertainty, diversity and density into batch mode active learning in relevance feedback. Extensive experiments on publicly available datasets show that the proposed method is promising.

► We propose degree of certainty asymmetric propagation to model these criteria.
► We incorporate uncertainty, diversity, and density to unify a formulation.
► We consider the correlation between labeled samples and unlabeled samples.