By Topic

Semantic Indexing of News Video Sequences: A Multimodal Hierarchical Approach Based on Hidden Markov Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
M. H. Kolekar ; Student Member, IEEE ; S. Sengupta

In this paper, we propose a system that semantically classifies news video at different layers of semantic significance, using different elements of visual content. The classification hierarchy generates low-level concepts, and concept hierarchy generates high-level concepts from low-level concepts. Our classification hierarchy is based on a few popular audio and video content analysis techniques like short-time energy and zero crossing rate (ZCR) for audio and hidden Markov model (HMM) based techniques for video, using color and motion as features. From these low-level concepts of classification hierarchy, rule-based classifier generates higher-level concepts. The proposed framework has been successfully tested with TV news video sequences. Our results are very promising. We have used top-down hierarchical approach, which permits us to avoid shot detection and clustering and consequently improves the classification performance. This has created the great opportunity for supporting more powerful video search engines.

Published in:

TENCON 2005 - 2005 IEEE Region 10 Conference

Date of Conference:

21-24 Nov. 2005