%0 Journal Article
%T Automatic Semantic Image Annotation with Granular Analysis Method<br>图像语义自动标注及其粒度分析方法
%A ZHANG Su-Lan
%A GUO Ping
%A ZHANG Ji-Fu
%A HU Li-Hua
%A <br>张素兰
%A 郭平
%A 张继福
%A 胡立华
%J 自动化学报
%D 2012
%I 
%X To bridge the semantic gap between low-level visual feature and high-level semantic concepts has been the subject of intensive investigation for years in order to improve the accuracy of automatic image annotation and satisfy the users' needs of quick image retrieval. Granular analysis is a hierarchical and important data analyzing method, which provides a new idea and method for solving the complicated problem. The accuracy of automatic image annotation and the efficiency of image retrieval are varying with the granularity size of image understanding and analysis. In this paper, the state-of-art models of automatic semantic image annotation are overviewed, then the idea and models of the granular analysis with its application in the process of automatic semantic image annotation are discussed, and the granular analysis based automatic image annotation methods are investigated as well as the promising research directions are given.
%K Content-based image retrieval (CBIR)
%K automatic semantic image annotation
%K granular analysis
%K granular computing model<br>图像内容检索
%K 自动图像语义标注
%K 粒度分析
%K 粒计算模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=1393146D229514EDB1B80F0755270496&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=94C357A881DFC066&sid=2A22E972FD97071B&eid=780091CB32840698&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=80