Cross-media topic analysis and information retrieval

Research on cross-media topic analysis and information retrieval methods, which utilize semantic of multimedia data to describe cross-media documents. As the emerging of multimedia data on Internet, text based methods cause the problem of inadequacy of semantic, so a uniform semantic presentation on different media data is very important for information retrieval system. Cross-media topic analysis and information retrieval methods are proposed in this paper. Firstly, generative method and visual topic learning algorithm are used 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, cross-media topic tagging and integrated search are achieved. Using the proposed method, a food safety information retrieval system is established and experiment results also show its effectiveness.