A semantic content analysis model for sports video based on perception concepts and finite state machines
Bai, Liang, Lao, Songyang, Jones, Gareth J.F.ORCID: 0000-0003-2923-8365 and Smeaton, Alan F.ORCID: 0000-0003-1028-8389
(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.
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
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 25 Oct 2018 12:14