Improving Relevance Feedback for Image Retrieval with Asymmetric Sampling

Relevance feedback is a quite effective approach to improve performance for image retrieval. Recently, active learning method has attracted much attention due to its capability of alleviating the burden of labeling in relevance feedback. However, most of the traditional studies focus on single sample selection in each feedback which needs heavy computational cost in practice. In this paper, we presents a novel batch mode active learning method for informative sample selection. Inspired by graph propagation, we consider the certainty of labels as asymmetric propagation information on graph, and formulate the correlation between labeled samples and unlabeled samples in an united scheme. Extensive experiments on publicly available data sets show that the proposed method is promising.