@inproceedings{ef5d402f87844f158aaa0e8f51480a46,
title = "Semi-supervised learning in traffic scene surveillance based on label-propagation",
abstract = "Object classification in traffic scene surveillance has attracted much attention recent years. Traditional classification methods need lots of labeled samples to build a satisfying classifier. However, the acquisition of the labeled samples may cost lots of time and human labor. In this paper, we propose an label-propagation based semi-supervised learning method which uses the information of both labeled and un-labeled samples. Experiment results show that our method outperforms the traditional methods both in accuracy and robustness.",
keywords = "label propagation, object classification, semi-supervised learning, traffic scene surveillance",
author = "Meng Liang and Zhaoxiang Zhang and Yunhong Wang",
year = "2013",
doi = "10.1109/ICIP.2013.6738889",
language = "英语",
isbn = "9781479923410",
series = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
publisher = "IEEE Computer Society",
pages = "4317--4320",
booktitle = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
address = "美国",
note = "2013 20th IEEE International Conference on Image Processing, ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",
}