Batch Mode Active Learning for Multi-Label Image Classification with Informative Label Correlation Mining
Abstract
The performances of supervised learning techniques on image classification problems heavily rely on the quality of their training images. But the acquisition of high quality training images requires significant efforts from human annotators. In this paper, we propose a novel multi-label batch model active learning (MLBAL) approach that allows the learning algorithm to actively select a batch of informative example-label pairs from which it learns at each learning iteration, so as to learn accurate classifiers with less annotation efforts. Unlike existing methods, the proposed approach fines the active selection granularity from example to example-label pair, and takes into account the informative label correlations for active learning. And the empirical studies demonstrate its effectiveness.
Cite
Text
Zhang et al. "Batch Mode Active Learning for Multi-Label Image Classification with Informative Label Correlation Mining." IEEE/CVF Winter Conference on Applications of Computer Vision, 2012. doi:10.1109/WACV.2012.6163043Markdown
[Zhang et al. "Batch Mode Active Learning for Multi-Label Image Classification with Informative Label Correlation Mining." IEEE/CVF Winter Conference on Applications of Computer Vision, 2012.](https://mlanthology.org/wacv/2012/zhang2012wacv-batch/) doi:10.1109/WACV.2012.6163043BibTeX
@inproceedings{zhang2012wacv-batch,
title = {{Batch Mode Active Learning for Multi-Label Image Classification with Informative Label Correlation Mining}},
author = {Zhang, Bang and Wang, Yang and Wang, Wei},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2012},
pages = {401-407},
doi = {10.1109/WACV.2012.6163043},
url = {https://mlanthology.org/wacv/2012/zhang2012wacv-batch/}
}