Image labeling survey in the Journal of Business Analytics

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 recognizing image content has led to the emergence of many ad-hoc labelling tools.

For this reason, together with my co-authors Christoph Sager and Christian Janiesch, we conducted a comprehensive survey to capture and systematize the commonalities as well as the distinctions between existing image labelling software. We performed a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structured the manual labelling task by its organization of work, user interface design options, and user support techniques to derive a systematization 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.

Our article is freely available with open access: Have fun reading 🙂