id: 06104149
dt: a
an: 06104149
au: Zhao, Rui; Wei, Zhihua; Miao, Duoqian; Wu, Yan; Mei, Lin
ti: Semi-supervised vehicle recognition: an approximate region constrained
    approach.
so: Li, Tianrui (ed.) et al., Rough sets and knowledge technology. 7th
    international conference, RSKT 2012, Chengdu, China, August 17‒20,
    2012. Proceedings. Berlin: Springer (ISBN 978-3-642-31899-3/pbk).
    Lecture Notes in Computer Science 7414. Lecture Notes in Artificial
    Intelligence, 161-166 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: Semi-supervised learning; object recognition; approximate region interval;
    constraints
ci: 
li: doi:10.1007/978-3-642-31900-6_21
ab: Summary: Semi-supervised learning attracts much concern because it can
    improve classification performance by using unlabeled examples. A novel
    semi-supervised classification algorithm SsL-ARC is proposed for
    real-time vehicle recognition. It makes use of the prior information of
    object vehicle moving trajectory as constraints to bootstrap the
    classifier in each iteration. Approximate region interval of trajectory
    are defined as constraints. Experiments on real world traffic
    surveillance videos are performed and the results verify that the
    proposed algorithm has the comparable performance to the
    state-of-the-art algorithms.
rv: