%0 Journal Article
%T Neighborhood Preserving-based Relevance Feedback Algorithm in Image Retrieval<br>一种基于近邻保留的相关反馈图像检索算法
%A 鲁坷
%A 赵继东
%A 丁正明
%A 吴跃
%J 计算机科学
%D 2012
%I 
%X When there are no sufficient feedback samples provided by Relevance feedback, supervised learning methods may suffer from the over-fitting in image retrieval. This paper proposed a novel neighborhood preserving regression algorithm which makes efficient use of unlabeled images. The algorithm selects the function which can minimize the empirical loss on the labeled images, thus, the function can respect both semantic and geometrical structures of the image database. The experimental results show that the algorithm is effective for image retrieval.
%K Manifold learning
%K Neighborhood preserving
%K Relevance feedback
%K Image retrieval<br>流形学习，近部保留，相关反馈，图像检索
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=1A50AD49C0C94949B29F6C63E8D5256E&yid=99E9153A83D4CB11&vid=7C3A4C1EE6A45749&iid=CA4FD0336C81A37A&sid=C1B34927D429E92F&eid=03E56C113B4E5A88&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=18