%0 Journal Article
%T Boosting-based Automatic Linguistic Indexing of Pictures<br>基于Boosting学习的图片自动语义标注
%A RU Li-yun
%A MA Shao-ping
%A LU Jing
%A <br>茹立云
%A 马少平
%A 路晶
%J 中国图象图形学报
%D 2006
%I 
%X Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in content-based image retrieval.In this paper,a boosting-based automatic linguistic indexing approach is proposed and a linguistic indexing system called BLIR(Boosting for Linguistic indexing Image Retrieval system) is built.It is assumed that images of same semantic meaning can be represented by a model combined with a group of features.2D-MHMM model is found to be such a template for one special kind of color and texture combination,which corresponds to one cluster in feature space.Thus in BLIR system, a large number of 2D-MHMM models are generated and a boosting algorithm is used to associate keyword with models.The system has been implemented and tested on a photographic image database of about(60 000) images.Results demonstrate the effectiveness of the proposed technique which outperforms other approaches.
%K content-based image retrieval
%K linguistic indexing of pictures
%K Bootsting algorithm
%K two-dimensional multi-resolution hidden Markov model(2D-MHMM)<br>基于内容图像检索
%K 图像语义标注
%K Boosting算法
%K 2维多分辨率隐马尔科夫模型(2D-MHMM)
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=AEC32132F4C2D028&yid=37904DC365DD7266&vid=708DD6B15D2464E8&iid=E158A972A605785F&sid=8C8D895E58E44DBB&eid=ABF2590617D31FFD&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=12