Video Segmentation and Key Frame Extraction with

In this paper, a parametric model for video segmentation and key frame extraction in the video content analysis is proposed. The autoregressive (AR) modeling is used to model the feature sequence of frames over time and to make the future content analysis in the AR parametric space. Based on this parametric framework, detecting shot boundaries in video sequences and extracting key frames from shots are conducted. Real experiments results are presented to illustrate the good performance of this new method.

[1]  Giuseppe Caccia,et al.  Key frames extraction in athletic video , 2003, Visual Communications and Image Processing.

[2]  Chong-Wah Ngo,et al.  Detection of gradual transitions through temporal slice analysis , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  Yap-Peng Tan,et al.  An effective post-refinement method for shot boundary detection , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Haibin Lu,et al.  Robust gradual scene change detection , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[5]  Hui Fang,et al.  A fuzzy logic approach for detection of video shot boundaries , 2006, Pattern Recognit..

[6]  Ioannis Pitas,et al.  Shot detection in video sequences using entropy based metrics , 2002, Proceedings. International Conference on Image Processing.

[7]  Richard J. Martin.  A metric for ARMA processes , 2000, IEEE Trans. Signal Process..

[8]  Borko Furht,et al.  Video and Image Processing in Multimedia Systems , 1995 .

[9]  Xu De,et al.  Efficient key-frame extraction using density-estimation-based nonparametric clustering , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[10]  Thomas Kailath,et al.  Modern signal processing , 1985 .