A survey of image labelling for computer vision applications

TL;DR

This survey systematically reviews image labelling tools for computer vision, highlighting their features, application domains, and categorizing existing software to guide future developments.

Contribution

It provides a structured classification of image labelling software and identifies key application archetypes and domains, filling a gap in comprehensive understanding.

Findings

Identified common features and distinctions among labelling tools

Classified software into application archetypes and domains

Uncovered key design options and support techniques

Abstract

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.