%0 Journal Article
%T Image Re-ranking Based on Extraction of Semantic Regions<br>基于语义区域提取的图像重排
%A CHEN Zeng
%A HOU Jin
%A ZHANG Deng-Sheng
%A ZHANG Hua-Zhong
%A <br>陈曾
%A 侯进
%A 张登胜
%A 张华忠
%J 自动化学报
%D 2011
%I 
%X It is difficult for current image search engines to accurately grasp the real intention of users. Based on the search results, we propose three clustering algorithms to extract semantic regions of Web images. These methods include K-means clustering with determined k centers, expectation maximization clustering with the determined parameters, and semi-supervised K-means clustering. We then select the salient regions with the high salient scores as the semantic regions. We demonstrate the experimental results by comparing the three clustering algorithms. The proposed image re-ranking system can more accurately show the ordered search results than web image engines.
%K Extraction of semantic regions
%K semi-supervised clustering
%K K-means clustering
%K expectation maximization clustering
%K image re-ranking<br>语义区域提取
%K 半监督聚类
%K K-means聚类
%K 最大期望聚类
%K 图像重排
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=B3C7018AFD6E563961BE9328BBA2AB4E&yid=9377ED8094509821&vid=42425781F0B1C26E&iid=708DD6B15D2464E8&sid=37E1CFF130ACDBB2&eid=D98C4F25072149E5&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=8