ROOSTER: An image labeler and classifier through interactive recurrent annotation

Abstract

A large amount of training data is usually lacking at the beginning of system development and labeling such a large number of RGB (red, green, blue) images is laborious. Interactive recurrent annotation is beneficial to incrementally gain training images in the stream of the system development and provides an opportunity to reduce human workload. We developed a software package, ROOSTER, to integrate both labeling and prediction in a single user-friendly graphic user interface with interactive deep learning to reduce the laborious human labeling for fast development of machine vision systems. Predictions can be performed under both single-image mode and batch mode for multiple images. The prediction results can be used as the initial image labeling and manually adjusted under a single image mode. Human labeling and machine predictions are visualized on the same image. ROOSTER provides fully automatic labeling for abundantly available initial images of wheat stripe rust to gain essential predictability. The navigation of integrating prediction with labeling benefits human adjustment to iteratively improve predictability. The development of a detection system for wheat stripe rust was presented as a use case to demonstrate the efficiency of using interactive deep learning to develop machine vision systems.