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A survey of image labelling for computer vision applications

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  • Christoph Sager
  • Christian Janiesch
  • Patrick Zschech

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.

Suggested Citation

  • Christoph Sager & Christian Janiesch & Patrick Zschech, 2021. "A survey of image labelling for computer vision applications," Journal of Business Analytics, Taylor & Francis Journals, vol. 4(2), pages 91-110, July.
    • Handle: RePEc:taf:tjbaxx:v:4:y:2021:i:2:p:91-110
    DOI: 10.1080/2573234X.2021.1908861
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    Cited by:

    1. Muhammad Aasem & Muhammad Javed Iqbal & Iftikhar Ahmad & Madini O. Alassafi & Ahmed Alhomoud, 2022. "A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning," Mathematics, MDPI, vol. 10(24), pages 1-29, December.

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