Bayesian modeling of video editing and structure: semantic features for video summarization and browsing
The ability to model content semantics is an important step towards the development of intelligent interfaces to large image and video databases. While an extremely difficult problem in the abstract, semantic characterization is possible in domains where a significant amount of structure is exhibited by the content. Whenever this is the case, given their ability to integrate prior knowledge about this structure in the inferences to be made, Bayesian methods are a natural solution to the problem. We present a Bayesian architecture for content characterization and analyze its potential as a tool for accessing and browsing through video databases on a semantic basis.