Study of Cross-Media Topic Analysis Based on Visual Topic Model
Research on cross-media topic analysis methods, which utilize semantic of multimedia data to describe topics of cross-media documents. As the emerge of food safety related multimedia data, topic analysis based on single media data can’t obtain full topics, causing the problem of inadequacy of semantic. A cross-media topic analysis framework is proposed in this paper. Firstly, generative methods are used to get semantic of text and image data respectively. Then a visual topic learning algorithm is presented to construct visual topic model and map visual data to text topics. This method can solve the problem of consistent semantic description of cross-media data. On this basis, food safety topic tracking is achieved and experiment results also show its effectiveness.
Cross-media Topic analysis Visual topic Food safety
Yipeng Zhou Meiyu Liang Junping Du
School of Computer Science and Information Engineering,Beijing Technology and Business University, B Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia,Beijing University Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
3479-3482
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）