Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
The load unbalancing problem for region growing image segmentation algorithms
Journal of Parallel and Distributed Computing
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
Robust Playfield Segmentation using MAP Adaptation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Scene-based event detection for baseball videos
Journal of Visual Communication and Image Representation
Video scene segmentation and semantic representation using a novel scheme
Multimedia Tools and Applications
A unified framework for semantic shot classification in sports video
IEEE Transactions on Multimedia
Semantic analysis of soccer video using dynamic Bayesian network
IEEE Transactions on Multimedia
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
Digital image processing techniques for the detection and removal of cracks in digitized paintings
IEEE Transactions on Image Processing
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
A novel video key-frame-extraction algorithm based on perceived motion energy model
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
A new key frame representation for video segment retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present an effective and efficient framework for baseball video scene classification. The results of scene classification can be able to provide the ground for baseball video abstraction and high-level event extraction. In general, most conventional approaches are shot-based, which shot change detection and key-frame extraction are necessary prerequisite procedures. On the contrary, we propose a frame-based approach. In our scene classification framework, an efficient playfield segmentation technique is proposed, and then the reduced field maps are utilized as scene templates. Because the shot change detection and the key-frame extraction are not required in proposed method, the new framework is very simple and efficient. The experimental results have demonstrated that the effectiveness of our proposed framework for baseball videos scene classification, and it can be easily extended the template-based approach to other kinds of sports videos.