  Manual annotation of bounding boxes for object detection in digital images is
tedious, and time and resource consuming. In this paper, we propose a
semi-automatic method for efficient bounding box annotation. The method trains
the object detector iteratively on small batches of labeled images and learns
to propose bounding boxes for the next batch, after which the human annotator
only needs to correct possible errors. We propose an experimental setup for
simulating the human actions and use it for comparing different iteration
strategies, such as the order in which the data is presented to the annotator.
We experiment on our method with three datasets and show that it can reduce the
human annotation effort significantly, saving up to 75% of total manual
annotation work.
