Key-Frame Based Video Summarization Using QR-Decomposition

In this paper, we propose a novel keyframe based video summarization system using QR-Decomposition. Specially, we attend to the challenges of defining some measures to detect the dynamicity of shot and video and extracting appropriate keyframes that assure the purity of video summary. We derive some efficient measures to compute the dynam - icity of video shots using QR-Decomposition and we utilize it in detecting the number of keyframes must be selected from each shot. Also, we derive a corollary that illustrates a new property of QR-Decomposition. We utilize this property in order to summarize video shots with low redundancy. The proposed algorithm is implemented and evaluated on TRECVID 2006 benchmark platform. Compared with results reported by others, our results are among the best. These results confirm the high performance of the proposed algorithm.

[1]  Michael J. Black,et al.  Summarization of videotaped presentations: automatic analysis of motion and gesture , 1998, IEEE Trans. Circuits Syst. Video Technol..

[2]  Mahmood Fathy,et al.  Video Shot Boundary Detection Using QR-Decomposition and Gaussian Transition Detection , 2010, EURASIP J. Adv. Signal Process..

[3]  Tie-Yan Liu,et al.  Shot reconstruction degree: a novel criterion for key frame selection , 2004, Pattern Recognit. Lett..

[4]  Sang Uk Lee,et al.  Efficient video indexing scheme for content-based retrieval , 1999, IEEE Trans. Circuits Syst. Video Technol..

[5]  Andreas Girgensohn,et al.  Time-Constrained Keyframe Selection Technique , 2004, Multimedia Tools and Applications.

[6]  Ioannis Pitas,et al.  Information theory-based shot cut/fade detection and video summarization , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Michael Mills,et al.  A magnifier tool for video data , 1992, CHI.

[8]  SangKeun Lee,et al.  Properties of the singular value decomposition for efficient data clustering , 2004, IEEE Signal Processing Letters.

[9]  Thomas S. Huang,et al.  Exploring video structure beyond the shots , 1998, Proceedings. IEEE International Conference on Multimedia Computing and Systems (Cat. No.98TB100241).

[10]  Yukinobu Taniguchi,et al.  An intuitive and efficient access interface to real-time incoming video based on automatic indexing , 1995, MULTIMEDIA '95.

[11]  Derrick J. Parkhurst,et al.  Scene content selected by active vision. , 2003, Spatial vision.

[12]  Alan Hanjalic,et al.  Shot-boundary detection: unraveled and resolved? , 2002, IEEE Trans. Circuits Syst. Video Technol..

[13]  J. Henderson,et al.  Initial scene representations facilitate eye movement guidance in visual search. , 2007, Journal of experimental psychology. Human perception and performance.

[14]  Andreas Girgensohn,et al.  Time-Constrained Keyframe Selection Technique , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[15]  Rama Chellappa,et al.  Unsupervised view and rate invariant clustering of video sequences q , 2009 .

[16]  Robert Babuska,et al.  Rule base reduction: some comments on the use of orthogonal transforms , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[17]  Shih-Ping Liou,et al.  Automatic key-frame selection for content-based video indexing and access , 1999, Electronic Imaging.

[18]  Yueting Zhuang,et al.  Adaptive key frame extraction using unsupervised clustering , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[19]  Guoliang Fan,et al.  Combined key-frame extraction and object-based video segmentation , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  De Xu,et al.  Information Theoretic Metrics in Shot Boundary Detection , 2005, KES.