  In this paper, we aim to develop a method for automatically detecting and
tracking topics in broadcast news. We present a hierarchical And-Or graph (AOG)
to jointly represent the latent structure of both texts and visuals. The AOG
embeds a context sensitive grammar that can describe the hierarchical
composition of news topics by semantic elements about people involved, related
places and what happened, and model contextual relationships between elements
in the hierarchy. We detect news topics through a cluster sampling process
which groups stories about closely related events. Swendsen-Wang Cuts (SWC), an
effective cluster sampling algorithm, is adopted for traversing the solution
space and obtaining optimal clustering solutions by maximizing a Bayesian
posterior probability. Topics are tracked to deal with the continuously updated
news streams. We generate topic trajectories to show how topics emerge, evolve
and disappear over time. The experimental results show that our method can
explicitly describe the textual and visual data in news videos and produce
meaningful topic trajectories. Our method achieves superior performance
compared to state-of-the-art methods on both a public dataset Reuters-21578 and
a self-collected dataset named UCLA Broadcast News Dataset.
