Abstract

We propose a semi-automatic method to obtain fore-ground object masks for a large set of related images. Wedevelop a stagewise active approach to propagation: ineach stage, we actively determine the images that appearmost valuable for human annotation, then revise the fore-ground estimates in all unlabeled images accordingly. Inorder to identify images that, once annotated, will propagate well to other examples, we introduce an active selection procedure that operates on the joint segmentation graph over all images. It prioritizes human intervention for those images that are uncertain and influential in the graph, while also mutually diverse. We apply our method to obtainforeground masks for over 1 million images. Our method yields state-of-the-art accuracy on the ImageNet and MIT Object Discovery datasets, and it focuses human attention more effectively than existing propagation strategies.