Rapid Annotation Tool to Train Novel Concept Detectors with Active Learning

Annotating a large set of images, especially with bounding boxes, is a tedious task. In this paper, we propose an intuitive image annotation tool. This tool not only allows (non-expert) users to annotate images with novel concepts, but is also able to achieve acceptable performance with a smaller number of annotated images. The tool can also propose detections on unannotated images, to provide faster annotation and insight in the performance of the system. The tool is based on a Single Shot Multi-box Detector (SSD) neural network with active learning, based on showing the images with high-confidence detections first, to have a fast verification and re-training. An experiment on simulated data shows that this active learning method can achieve higher performance in a shorter expected annotation time with a small number of images (less than 500). A small experiment on user annotated data shows that the annotation tool allows faster annotation compared to without the annotation tool