%0 Journal Article
%T Automatic Image Annotation Using Social Group Latent Topic Mining and Multi-group Information Fusion<br>基于社群隐含主题挖掘和多社群信息融合的自动图像标注
%A CHEN Ye
%A SHAO Jian
%A ZHU Ke
%A <br>陈烨
%A 邵健
%A 朱科
%J 中国图象图形学报
%D 2010
%I 
%X At photo sharing websites like Flickr, a lot of images can not be effectively used and retrieved due to lack of tags. In order to retrieve images effectively, this paper presents a novel social group latent topic mining and multi-group information fusion based automatic image annotation algorithm by exploiting the property that users in Flickr often recommend their uploaded pictures to associated social groups according to the hidden topics in each picture. Different from traditional automatic image annotation methods, this algorithm first adopts the latent Dirichlet allocation model to mine the latent topics in single social group and makes use of the hidden topics to filter initial noisy tags generated by tag propagation among similar images, then utilizes multi-group information fusion to generate the final annotations for images simultaneously belonging to multiple social groups. Experimental results show the effectiveness of this algorithm.
%K automatic image annotation
%K social group
%K latent topic mining
%K latent Dirichlet allocation
%K multi-group information fusion<br>自动图像标注
%K 社群
%K 潜在主题挖掘
%K 隐Dirichlet分配模型
%K 多社群信息融合
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=9ACE84D0791ABBC1FC2A451175D80D23&yid=140ECF96957D60B2&vid=23CCDDCD68FFCC2F&iid=B31275AF3241DB2D&sid=ABE2D40B2765724E&eid=114891522AE71A91&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=17