TY - GEN
T1 - FreeLabel
T2 - 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
AU - Dias, Philipe A.
AU - Shen, Zhou
AU - Tabb, Amy
AU - Medeiros, Henry
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/4
Y1 - 2019/3/4
N2 - 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 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.
AB - 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 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.
UR - http://www.scopus.com/inward/record.url?scp=85063594183&partnerID=8YFLogxK
U2 - 10.1109/WACV.2019.00010
DO - 10.1109/WACV.2019.00010
M3 - Conference contribution
AN - SCOPUS:85063594183
T3 - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
SP - 21
EP - 30
BT - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 January 2019 through 11 January 2019
ER -