Two important action scenes detection based on probability neural networks

Authors:
Yu-Liang Geng;De Xu;Jia-Zheng Yuan;Song-He Feng
Affiliations:
Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China;Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China;Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China;Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 6
Cited 0

Probabilistic neural networks

Neural Networks
A user attention model for video summarization

Proceedings of the tenth ACM international conference on Multimedia
Generative and Discriminative Modeling toward Semantic Context Detection in Audio Tracks

MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Effective video scene detection approach based on cinematic rules

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Salient region detection using weighted feature maps based on the human visual attention model

PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Content-based movie analysis and indexing based on audiovisual cues

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, an effective classification approach for action scenes is proposed, which exploits the film grammar used by filmmakers as guideline to extract features, detect and classify action scenes. First, action scenes are detected by analyzing film rhythm of video sequence. Then four important features are extracted to characterize chase and fight scenes. After then the Probability Neural Networks is employed to classify the detected action scenes into fight, chase and uncertain scenes. Experimental results show that the proposed method works well over the real movie videos.