Context-Based Interpretation and Indexing of Video Data
A. Mittal (IIT Roorkee, India, The National University of Singapore, Singapore, IIT Delhi, India), Cheong Loong Fah (The National University of Singapore, Singapore), Ashraf Kassim (The National University of Singapore, Singapore) and Krishnan V. Pagalthivarthi (Indian Institute of Technology, India)
Copyright: © 2008
Most of the video retrieval systems work with a single shot without considering the temporal context in which the shot appears. However, the meaning of a shot depends on the context in which it is situated and a change in the order of the shots within a scene changes the meaning of the shot. Recently, it has been shown that to find higher-level interpretations of a collection of shots (i.e., a sequence), intershot analysis is at least as important as intrashot analysis. Several such interpretations would be impossible without a context. Contextual characterization of video data involves extracting patterns in the temporal behavior of features of video and mapping these patterns to a high-level interpretation. A Dynamic Bayesian Network (DBN) framework is designed with the temporal context of a segment of a video considered at different granularity depending on the desired application. The novel applications of the system include classifying a group of shots called sequence and parsing a video program into individual segments by building a model of the video program.