  Supervised object detection has been proven to be successful in many
benchmark datasets achieving human-level performances. However, acquiring a
large amount of labeled image samples for supervised detection training is
tedious, time-consuming, and costly. In this paper, we propose an efficient
image selection approach that samples the most informative images from the
unlabeled dataset and utilizes human-machine collaboration in an iterative
train-annotate loop. Image features are extracted by the CNN network followed
by the similarity score calculation, Euclidean distance. Unlabeled images are
then sampled into different approaches based on the similarity score. The
proposed approach is straightforward, simple and sampling takes place prior to
the network training. Experiments on datasets show that our method can reduce
up to 80% of manual annotation workload, compared to full manual labeling
setting, and performs better than random sampling.
