@inproceedings {pub3908,
	title = {Active Learning for Image Recognition using a Visualization-Based User Interface},
	author = {Christian  Limberg AND Heiko Wersing AND Helge Ritter},
	year = {2019},
	month = {September},
	abstract = {This paper introduces a novel approach for querying samples to be labeled in active learning for image recognition. By using dimension reduction techniques to create a 2D feature embedding for visualization, the user is able to efficiently label images for training a classifier. This is made possible by a querying strategy specifically designed for the visualization, seeking optimized bounding-box views for subsequent labeling. The approach is implemented in a web-based prototype. It is compared in-depth to other active learning querying strategies within a user study we did with 31 participants. With our approach the participants could train a more accurate classifier than with the other approaches on a challenging data set. Additionally, we demonstrate that due to the visualization, the number of labeled samples increases and also the label quality improves.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {International Conference on Artificial Neural Networks}
}