Neural Network Based Temporal Video Segmentation

The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences.

[1]  Nilesh V. Patel,et al.  Compressed Video Processing for Cut Detection , 1996 .

[2]  Ramesh C. Jain,et al.  Knowledge-guided parsing in video databases , 1993, Electronic Imaging.

[3]  Stephen W. Smoliar,et al.  An integrated system for content-based video retrieval and browsing , 1997, Pattern Recognit..

[4]  Charles A. Bouman,et al.  ViBE: a new paradigm for video database browsing and search , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

[5]  Irena Koprinska,et al.  Temporal video segmentation: A survey , 2001, Signal Process. Image Commun..

[6]  Ullas Gargi,et al.  Performance characterization of video-shot-change detection methods , 2000, IEEE Trans. Circuits Syst. Video Technol..

[7]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[8]  Ralph M. Ford,et al.  Metrics for shot boundary detection in digital video sequences , 2000, Multimedia Systems.

[9]  Alan Hanjalic,et al.  Automated high-level movie segmentation for advanced video-retrieval systems , 1999, IEEE Trans. Circuits Syst. Video Technol..

[10]  Thomas Martinetz,et al.  'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.

[11]  Boon-Lock Yeo,et al.  Rapid scene analysis on compressed video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[12]  A. Murat Tekalp,et al.  Temporal video segmentation using unsupervised clustering and semantic object tracking , 1998, J. Electronic Imaging.

[13]  Ramin Zabih,et al.  A feature-based algorithm for detecting and classifying production effects , 1999, Multimedia Systems.