Home video structuring with a two-layer shot clustering approach

Content-based video structuring above shot level faces technical challenges in semantic feature extraction and flexible shot cluster organization. Aiming at solving these problems, a two-layer shot clustering approach for home video structuring, which operates directly in MPEG domain, is presented in this paper. Such an approach goes one-step further than conversional one-layer structure to constructs a hierarchical content structure to represent more details of video contents as well as their interior correlations. With two independent aspects of human perception taken into consideration, this structure provides fine-grained organization of video shots. Promising results are achieved in the experiments made on MPEG-7 test videos.

[1]  Yu-Jin Zhang,et al.  Camera attention weighted strategy for video shot grouping , 2005, Visual Communications and Image Processing.

[2]  Alexander C. Loui,et al.  Finding structure in home videos by probabilistic hierarchical clustering , 2003, IEEE Trans. Circuits Syst. Video Technol..

[3]  Shih-Fu Chang,et al.  A fully automated content-based video search engine supporting spatiotemporal queries , 1998, IEEE Trans. Circuits Syst. Video Technol..

[4]  Li Bo,et al.  A practical algorithm for exception event detection for the home video security surveillance , 2001, 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479).

[5]  Lorenzo Favalli,et al.  Object tracking for retrieval applications in MPEG-2 , 2000, IEEE Trans. Circuits Syst. Video Technol..

[6]  Peng Wu A semi-automatic approach to detect highlights for home video annotation , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Charles A. Bouman,et al.  ViBE: a compressed video database structured for active browsing and search , 2004, IEEE Transactions on Multimedia.

[8]  Li Zhao,et al.  Key-frame extraction and shot retrieval using nearest feature line (NFL) , 2000, MULTIMEDIA '00.

[9]  Dan Schonfeld,et al.  VORTEX: Video Retrieval and Tracking from Compressed Multimedia Databases - Multiple Object Tracking from MPEG-2 Bit Stream , 2000, J. Vis. Commun. Image Represent..

[10]  Shih-Fu Chang,et al.  Survey of compressed-domain features used in audio-visual indexing and analysis , 2003, J. Vis. Commun. Image Represent..

[11]  Chun Chen,et al.  Audio and video combined for home video abstraction , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[12]  Janko Calic,et al.  Efficient key-frame extraction and video analysis , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

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

[14]  Chong-Wah Ngo,et al.  Motion-Based Video Representation for Scene Change Detection , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  Zhu Liu,et al.  Integration of audio and visual information for content-based video segmentation , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[16]  Wolfgang Effelsberg,et al.  Video abstracting , 1997, CACM.

[17]  HongJiang Zhang,et al.  Video scene extraction by force competition , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[18]  Lie Lu,et al.  Optimization-based automated home video editing system , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Shih-Fu Chang,et al.  Determining computable scenes in films and their structures using audio-visual memory models , 2000, ACM Multimedia.