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

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....

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Bibliographic Details
Published inTENCON 2005 - 2005 IEEE Region 10 Conference pp. 1 - 6
Main Authors Kolekar, M.H., Sengupta, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2005
Subjects
Broadcasting
Color
Content based retrieval
Content based video retrieval
Filters
Hidden Markov models
Indexing
Motion analysis
Multimedia communication
news classification
semantic
Testing
video indexing
Video sequences
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Summary: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.
ISBN:0780393112
9780780393110
ISSN:2159-3442
2159-3450
DOI:10.1109/TENCON.2005.301204