Knowledge Based Methods for Video Data Retrieval

Large collections of publicly available video data grow day by day, the need to query this data efficiently becomes significant. Consequently, content-based retrieval of video data turns out to be a challenging and important problem. This paper addresses the specific aspect of inferring semantics automatically from raw video data using different knowledge-based methods. In particular, this paper focuses on three techniques namely, rules, Hidden Markov Models (HMMs), and Dynamic Bayesian Networks (DBNs). First, a rule-based approach that supports spatio-temporal formalization of high-level concepts is introduced. Then the focus of this paper is towards stochastic methods and also demonstrates how HMMs and DBNs can be effectively used for content-based video retrieval from multimedia databases.