Key-frame extraction and video summarization using QR-Decomposition

This paper explores the problem of high quality key frame extraction and video summarization detection and examines a novel video summarization algorithm by using QR-decomposition. We derive some efficient measures to compute the dynamicity of video shots using QR-Decomposition and we utilize it in detecting the number of keyframes could be selected from each shot. Also, we conclude 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. The results confirm the high performance of the proposed algorithm.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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