@inproceedings{1f689175d0fd429d8dc60893c686dc5f,
title = "Maximum classification optimization-based active learning for image classification",
abstract = "Traditional multi-class image classification needs a large number of training samples for building a classifier model. However, it is very time-consuming and costly to obtain labels for a large number of training samples from human experts. Active learning is a feasible solution. This paper proposes a maximum classification optimization method (MCO) for actively selecting unlabeled images to acquire labels. It integrated the information of an unlabeled sample from different perspectives with two steps. It first chooses a subset of candidates, and then selects the best from these candidates. Our experimental results show that the maximum classification optimization method outperforms two popular exiting methods (entropy-based uncertainty and BvSB).",
keywords = "BvSB, active learning, image classification, maximum classification optimization",
author = "Zhengwei Cui and Xiaoming Chen and Jian Wu and Sheng, {Victor S.} and Yujie Shi",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 7th International Congress on Image and Signal Processing, CISP 2014 ; Conference date: 14-10-2014 Through 16-10-2014",
year = "2014",
month = jan,
day = "6",
doi = "10.1109/CISP.2014.7003879",
language = "English",
series = "Proceedings - 2014 7th International Congress on Image and Signal Processing, CISP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "759--764",
editor = "Yi Wan and Jinguang Sun and Jingchang Nan and Quangui Zhang and Liangshan Shao and Lipo Wang",
booktitle = "Proceedings - 2014 7th International Congress on Image and Signal Processing, CISP 2014",
}