%0 Journal Article
%T Content-based remote sensing image retrieval using co-training of multiple classifiers<br>多分类器实例协同训练遥感图像检索
%A LI Shijin
%A TAO Jian
%A WAN Dingsheng
%A FENGJun
%A <br>李士进
%A 陶 剑
%A 万定生
%A 冯 钧
%J 遥感学报
%D 2010
%I 
%X There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training. In this paper a novel approach using co-training in multiple classifier systems is presented, which can label the unclassified samples automatically by using the cooperative determination of the classifiers which are created on several different feature sets, so that the small sample problem can be raveled out. Compared with the technique of relevance feedback, the experiments indicate that they have their own strengths and can obtain almost the same results. However, the proposed approach of co-training in multiple classifier systems is superior in regard of avoiding the needs of human intervention through relevance feedback.
%K remote sensing
%K content-based image retrieval (CBIR)
%K co-training
%K multiple classifier systems
%K semi-supervised learning<br>遥感
%K 基于内容的图像检索
%K 协同训练
%K 多分类器
%K 半监督学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=A41A70F4AB56AB1B&jid=F926358B31AC94511E4382C083F7683C&aid=9EEB5667A2899E5A7F64674164BB894D&yid=140ECF96957D60B2&vid=F3583C8E78166B9E&iid=38B194292C032A66&sid=8566B4AE2A8832E3&eid=D8AE57480552698F&journal_id=1007-4619&journal_name=遥感学报&referenced_num=1&reference_num=21