TY - GEN
T1 - Noise correction of image labeling in crowdsourcing
AU - Nicholson, Bryce
AU - Sheng, Victor S.
AU - Zhang, Jing
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/9
Y1 - 2015/12/9
N2 - We investigate the methods of improving data quality, in terms of label accuracy, in the context of image labeling in crowdsourcing. First, we look at three consensus methods for inferring a ground-truth label from the multiple noisy labels obtained from crowdsourcing, i.e., Majority Voting (MV), Dawid Skene (DS), and KOS. We then apply three noise correction methods to correct labels inferred by these consensus methods, i.e., Polishing Labels (PL), Self-Training Correction (STC), and Cluster Correction (CC). Our experimental results show that the noise correction methods improve the labeling quality significantly.
AB - We investigate the methods of improving data quality, in terms of label accuracy, in the context of image labeling in crowdsourcing. First, we look at three consensus methods for inferring a ground-truth label from the multiple noisy labels obtained from crowdsourcing, i.e., Majority Voting (MV), Dawid Skene (DS), and KOS. We then apply three noise correction methods to correct labels inferred by these consensus methods, i.e., Polishing Labels (PL), Self-Training Correction (STC), and Cluster Correction (CC). Our experimental results show that the noise correction methods improve the labeling quality significantly.
UR - http://www.scopus.com/inward/record.url?scp=84956691648&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2015.7351042
DO - 10.1109/ICIP.2015.7351042
M3 - Conference contribution
AN - SCOPUS:84956691648
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1458
EP - 1462
BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PB - IEEE Computer Society
T2 - IEEE International Conference on Image Processing, ICIP 2015
Y2 - 27 September 2015 through 30 September 2015
ER -