Abstract

Recently active learning has attracted a lot of attention in computer vision fifield, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computer vision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper, we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classififications. Our experiments on two essential tasks of computer vision, object recognition and scene recognition, demonstrate the effificacy of the proposed approach.