A Bayesian Framework for Semantic Content Characterization

Current systems for content ltering, browsing, and retrieval rely on low-level image descriptors which are unintuitive for most users. In this paper, we propose an alternative framework that exploits the struc-tured nature of most content sources to achieve semantic content characterization, and lead to much more meaningful user interaction. Computationally, this framework is based on the principles of Bayesian inference and can be implemented eeciently with Bayesian networks. As an illustration of its potential we apply it to the domain of movie databases.