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.

Plant phenotyping relevance

植物病害（コムギ縞萎縮病）の画像検出を対象とした対話型画像ラベリング・分類ソフトウェアを開発しており、植物病害状態の取得手法が中心である。

Code and data availability

The paper's wheat stripe rust use case is supported by a public Zenodo underlying dataset (400 author-captured training images and use case output files) and public author source code (zzlab.net, GitHub, archived Zenodo). The independent test data from Schirrmann et al. is only available on request.