Interactive rodent behavior annotation in video using active learning

Manual annotation of rodent behaviors in video is time-consuming. By learning a classifier, we can automate the labeling process. Still, this strategy requires a sufficient number of labeled examples. Moreover, we need to train new classifiers when there is a change in the set of behaviors that we consider or in the manifestation of these behaviors in video. Consequently, there is a need for an efficient way to annotate rodent behaviors. In this paper we introduce a framework for interactive behavior annotation in video based on active learning. By putting a human in the loop, we alternate between learning and labeling. We apply the framework to three rodent behavior datasets and show that we can train accurate behavior classifiers with a strongly reduced number of labeled samples. We confirm the efficacy of the tool in a user study demonstrating that interactive annotation facilitates efficient, high-quality behavior measurements in practice.