A Cost-Effective, Fast, and Robust Annotation Tool

Said, Asaad F.; Kashyap, Vinay; Choudhury, Namrata; Akhbari, Farshad

doi:10.1109/aipr.2017.8457958

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Need For Image Labelling and Image Labelling Software1

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2021

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“…The labelling effort is mostly composed of the time spent by human workers and the associated monetary expenditure for wages and infrastructure. A common way to decrease these costs is to support the manual labelling with ILS and take the human more and more out of the process (Fiedler et al, 2019;Said et al, 2017;Son et al, 2018). As Q.…”

Section: Need For Image Labelling and Image Labelling Softwarementioning

confidence: 99%

A survey of image labelling for computer vision applications

Sager

Janiesch

Zschech

2021

Journal of Business Analytics

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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.

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Section: Need For Image Labelling and Image Labelling Softwarementioning

confidence: 99%