  Supervised machine learning methods for image analysis require large amounts
of labelled training data to solve computer vision problems. The recent rise of
deep learning algorithms for recognising image content has led to the emergence
of many ad-hoc labelling tools. With this survey, we capture and systematise
the commonalities as well as the distinctions between existing image labelling
software. We perform a structured literature review to compile the underlying
concepts and features of image labelling software such as annotation
expressiveness and degree of automation. We structure the manual labelling task
by its organisation of work, user interface design options, and user support
techniques to derive a systematisation schema for this survey. Applying it to
available software and the body of literature, enabled us to uncover several
application archetypes and key domains such as image retrieval or instance
identification in healthcare or television.
