An Assistive Learning Workflow on Annotating Images for Object Detection
Vivian W. Wong, Max K. Ferguson, Kincho H. Law, Yung-Tsun T. Lee
We present an end-to-end workflow to generate annotated image datasets for object detection. With this workflow, which we call assistive learning, we are able to reduce manual annotation time on two experimental datasets by 79.4% and 83.1%. The experimental results of this work show three contributions of the assistive learning workflow: (1) Savings on human annotation time; (2) generalizability to variable dataset sizes, domains and convolutional neural network (CNN) models; and (3) faster CNN training with limited amount of labeled data using a novel contextual sampling method, thereby a reduction in human workload early on in the assistive learning process. In addition, we wrap the workflow in an interactive annotation interface, allowing annotators without any machine learning experience to speed up the annotation process for training the CNN models.