Faceted topic retrieval of news video using joint topic modeling of visual features and speech transcripts

Because of the inherent ambiguity in user queries, an important task of modern retrieval systems is faceted topic retrieval (FTR), which relates to the goal of returning diverse or novel information elucidating the wide range of topics or facets of the query need. We introduce a generative model for hypothesizing facets in the (news) video domain by combining the complementary information in the visual keyframes and the speech transcripts. We evaluate the efficacy of our multimodal model on the standard TRECVID-2005 video corpus annotated with facets. We find that: (1) the joint modeling of the visual and text (speech transcripts) information can achieve significant F-score improvement over a text-alone system; (2) our model compares favorably with standard diverse ranking algorithms such as the MMR. Our FTR model has been implemented on a news search prototype that is undergoing commercial trial.