%0 Journal Article
%T Image Auto-Annotation via an Extended Generative Language Model<br>基于扩展生成语言模型的图像自动标注方法
%A WANG Mei
%A ZHOU Xiang-Dong
%A ZHANG Jun-Qi
%A XU Hong-Tao
%A SHI Bai-Le
%A <br>王梅
%A 周向东
%A 张军旗
%A 许红涛
%A 施伯乐
%J 软件学报
%D 2008
%I 
%X In this paper,based on the statistical smoothing strategy,a image region feature generative probability estimation method is proposed by exploiting maximum weight matching algorithm.By further analyzing and measuring the semantic correlations between words based on the training set,a novel image annotation algorithm for adopting the generative model is presented.The first annotation keyword is obtained by using the proposed image region feature generative probability estimation algorithm.Then,a heuristic iterate function is proposed to exploit the keyword semantic correlation.Finally,the semantic correlation between the annotation and the image can be improved by our annotation algorithm.The proposed annotation approach is tested on a real-world image database,and promising results are achieved.
%K image annotation
%K generative model
%K continuous feature estimation
%K maximum weight matching
%K semantic correlation<br>图像标注
%K 生成模型
%K 连续特征估计
%K 最大权匹配
%K 语义相关性
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=0F459030E5EFD5B2B316BCE95721CCD4&yid=67289AFF6305E306&vid=2A8D03AD8076A2E3&iid=9CF7A0430CBB2DFD&sid=3694286FAF8D37E3&eid=2C10B292C1342C17&journal_id=1000-9825&journal_name=软件学报&referenced_num=1&reference_num=26