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.