Institute of Electrical and Electronic Engineers (IEEE)
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks recently achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed to benefit the computer vision community, FreeLabel can be used for both crowdsourced or private annotation and has a modular structure that can be easily adapted for any image dataset.
Dias, Philipe A.; Shen, Zhou; Tabb, Amy; and Medeiros, Henry P., "FreeLabel: A Publicly Available Annotation Tool Based on Freehand Traces" (2019). Electrical and Computer Engineering Faculty Research and Publications. 535.
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Accepted version. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), (March 7 2019): 21-30. DOI. © 2019 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.