IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Context-Based Interpretation and Indexing of Video Data

Context-Based Interpretation and Indexing of Video Data
View Sample PDF
Author(s): 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
Pages: 20
Source title: Multimedia Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Mahbubur Rahman Syed (Minnesota State University Mankato, USA)
DOI: 10.4018/978-1-59904-953-3.ch039

Purchase

View Context-Based Interpretation and Indexing of Video Data on the publisher's website for pricing and purchasing information.

Abstract

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.

Related Content

K. Jairam Naik, Annukriti Soni. © 2021. 18 pages.
Randhir Kumar, Rakesh Tripathi. © 2021. 22 pages.
Yogesh Kumar Gupta. © 2021. 38 pages.
Kamel H. Rahouma, Ayman A. Ali. © 2021. 34 pages.
Muni Sekhar Velpuru. © 2021. 19 pages.
Vijayakumari B.. © 2021. 24 pages.
Neetu Faujdar, Anant Joshi. © 2021. 41 pages.
Body Bottom