Towards Scalable Dataset Construction: An Active Learning Approach
Abstract
As computer vision research considers more object categories and greater variation within object categories, it is clear that larger and more exhaustive datasets are necessary. However, the process of collecting such datasets is laborious and monotonous. We consider the setting in which many images have been automatically collected for a visual category (typically by automatic internet search), and we must separate relevant images from noise. We present a discriminative learning process which employs active, online learning to quickly classify many images with minimal user input. The principle advantage of this work over previous endeavors is its scalability. We demonstrate precision which is often superior to the state-of-the-art, with scalability which exceeds previous work.
Cite
Text
Collins et al. "Towards Scalable Dataset Construction: An Active Learning Approach." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88682-2_8Markdown
[Collins et al. "Towards Scalable Dataset Construction: An Active Learning Approach." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/collins2008eccv-scalable/) doi:10.1007/978-3-540-88682-2_8BibTeX
@inproceedings{collins2008eccv-scalable,
title = {{Towards Scalable Dataset Construction: An Active Learning Approach}},
author = {Collins, Brendan and Deng, Jia and Li, Kai and Fei-Fei, Li},
booktitle = {European Conference on Computer Vision},
year = {2008},
pages = {86-98},
doi = {10.1007/978-3-540-88682-2_8},
url = {https://mlanthology.org/eccv/2008/collins2008eccv-scalable/}
}