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A semantic content analysis model for sports video based on perception concepts and finite state machines

Bai, Liang and Lao, Songyang and Jones, Gareth J.F. and Smeaton, Alan F. (2007) A semantic content analysis model for sports video based on perception concepts and finite state machines. In: ICME 2007 - International Conference on Multimedia and Expo, 2-5 July 2007, Beijing, China.

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Abstract

In automatic video content analysis domain, the key challenges are how to recognize important objects and how to model the spatiotemporal relationships between them. In this paper we propose a semantic content analysis model based on Perception Concepts (PCs) and Finite State Machines (FSMs) to automatically describe and detect significant semantic content within sports video. PCs are defined to represent important semantic patterns for sports videos based on identifiable feature elements. PC-FSM models are designed to describe spatiotemporal relationships between PCs. And graph matching method is used to detect high-level semantic automatically. A particular strength of this approach is that users are able to design their own highlights and transfer the detection problem into a graph matching problem. Experimental results are used to illustrate the potential of this approach

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Digital video
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Publisher:Institute of Electrical and Electronics Engineers
Official URL:http://dx.doi.org/10.1109/ICME.2007.4284923
Copyright Information:Copyright © 2007 IEEE. Reprinted from ICME 2007 - International Conference on Multimedia and Expo. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Dublin City University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Funders:National High Technology Development 863 Program of China, National Natural Science Foundation of China, China Scholarship Council of China Education Ministry
ID Code:220
Deposited On:05 Mar 2008 by DORAS Administrator. Last Modified 05 May 2010 12:03

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