@inproceedings{b8ce67a88fa548bd8a24e3be1693e27c,
title = "Multi-label active learning for image classification with asymmetrical conditional dependence",
abstract = "Image classification is a hot topic of pattern recognition in computer vision. In order to achieve high accuracy of classification, a certain amount of high quality pictures are needed. As a matter of fact, high quality pictures are scarce. Active learning can solve such a problem. Label dependences play an important role in multi-label active learning for image classification. The interdependences between different labels are usually different and asymmetrical. This paper first brings the asymmetrical conditional label dependences into a novel active learning method for multi-label image classification based on the asymmetrical conditional label dependence, called ACDAL. Our extensive experimental results on three image and two non-image datasets show that our new approach ACDAL significantly outperforms existing approaches.",
keywords = "Active learning, asymmetrical label dependence, label correlation, multi-label image classification",
author = "Jian Wu and Shiquan Zhao and Sheng, {Victor S.} and Pengpeng Zhao and Zhiming Cui",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; null ; Conference date: 11-07-2016 Through 15-07-2016",
year = "2016",
month = aug,
day = "25",
doi = "10.1109/ICME.2016.7552899",
language = "English",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
booktitle = "2016 IEEE International Conference on Multimedia and Expo, ICME 2016",
}