Paper Info
Title
MSN: Statistical Understanding of Broadcasted Baseball Video Using Multi-Level Semantic Network
Abstract
The information processing of sports video yields valuable semantics for content delivery over narrowband networks. Traditional image/video processing is formulated in terms of low-level features describing image/video structure and intensity, while the high-level knowledge such as common sense and human perceptual knowledge are encoded in abstract and nongeometric representations. The management of semantic information in video becomes more and more difficult because of the large difference in representations, levels of knowledge, and abstract episodes. This paper proposes a semantic highlight detection scheme using a Multi-level Semantic Network (MSN) for baseball video interpretation. The probabilistic structure can be applied for highlight detection and shot classification. Satisfactory results will be shown to illustrate better performance compared with the traditional ones.
Year
DOI
Venue
2005
10.1109/TBC.2005.854169
TBC
Keywords
DocType
Volume
belief networks,content management,image classification,image representation,object detection,semantic networks,spatiotemporal phenomena,sport,statistical distributions,video signal processing,visual perception,Bayesian belief network,MSN,baseball video broadcasting,content delivery,highlight detection scheme,human perceptual knowledge,image-video structure,information processing,multilevel semantic network,narrowband network,nongeometric representation,probabilistic structure,semantic information management,shot classification,spatio-temporal analysis,sports video,statistical model understanding,Baseball video,Bayesian belief network,multi-level semantic network,spatio-temporal analysis,sport video,statistical modeling
Journal
51
Issue
ISSN
Citations 
4
0018-9316
21
PageRank 
References 
Authors
1.10
14
2
Name
Order
Citations
PageRank
Huang-Chia Shih118721.98
Chung-Lin Huang254037.61