Effective image annotation via active learning

Images must be annotated to support keyword searches. Sometimes, annotation can be extracted from the surrounding text, but often times, laborious manual annotation cannot be avoided. To minimize effort in manual annotation, we propose using active learning. Active learning selects the most semantically ambiguous images for users to label, and then propagates these labels to the rest of the images. Our experiments on a sample image-dataset show that active learning can drastically reduce manual annotation effort (by as much as 70%) to achieve high annotation accuracy.