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Shot Boundary Detection Using Octagon Square Search Pattern

Authors: J. Kavitha, S. Sowmyayani, P. Arockia Jansi Rani

Abstract:

In this paper, a shot boundary detection method is presented using octagon square search pattern. The color, edge, motion and texture features of each frame are extracted and used in shot boundary detection. The motion feature is extracted using octagon square search pattern. Then, the transition detection method is capable of detecting the shot or non-shot boundaries in the video using the feature weight values. Experimental results are evaluated in TRECVID video test set containing various types of shot transition with lighting effects, object and camera movement within the shots. Further, this paper compares the experimental results of the proposed method with existing methods. It shows that the proposed method outperforms the state-of-art methods for shot boundary detection.

Keywords: Content-based indexing and retrieval, cut transition detection, discrete wavelet transform, shot boundary detection, video source.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128083

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References:


[1] Sklar, Robert. Film: An International History of the Medium. (London): Thames and Hudson, (c. 1990). p. 526.
[2] Alan Hanjalic, “Shot Boundary detection: Unraveled and Resolved?”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 12, No. 2, February 2002.
[3] H. Zhang and S. Kankanhalli, “Automatic partitioning of full-motion video,” ACM Journal of Multimedia Systems, vol. 1, pp. 10–28, Jan. 1993.
[4] R. Zabih, J. Miller, and K. Mai, “A feature-based algorithm for detecting cuts and classifying scene breaks,” in Proc. ACM Multimedia ’95, San Francisco, CA, pp. 189–200, 1995.
[5] B. Shahraray, “Scene change detection and content-based sampling of video sequences,” Proc. SPIE, vol. 2419, pp. 2–13, Apr. 1995.
[6] A. Hampapur, R. Jain, T. Weymouth, Production model based digital video segmentation, J. Multimedia Tools Appl. 1 (1) (1995) 9–46.
[7] U. Gargi, R. Kasturi, and S. H. Strayer, “Performance characterization of video-shot-change detection methods,” IEEE Trans. Circuits, Systems, Video Technology, vol. 10, pp. 1–13, Feb. 2000.
[8] R. Lienhart, “Reliable transition detection in videos: A survey and practitioner’s guide,” Int. Journal of Image and Graphics, vol. 1, pp. 469–486, Sept. 2001.
[9] Weigang Zhang, et. al., “Video Shot Detection Using Hidden Markov Models with Complementary Features,” In Pro-ceedings of the First International Conference on Innovative Computing, Information and Control. Vol.3. http://doi.ieeecomputersociety.org/10.1109/ICICIC.2006.549, 2006.
[10] Y. Kawai, H. Sumiyoshi, and N. Yagi. “Shot Boundary De-tection at TRECVID 2007,” In TRECVID 2007 Workshop, Gaithersburg, 2007.
[11] Don, A., Uma, K. Adaptive edge-oriented shot boundary detection. EURASIP Journal on Image and Video Processing 2009.
[12] Shiguo Lian, “Automatic video temporal segmentation based on multiple features,” Soft Comput., vol. 15, no. 3, pp. 469–482, 2011.
[13] G. G. Lakshmi Priya , S. Domnic, “Transition Detection Using Hilbert Transform and Texture Features”, American Journal of Signal Processing, 2012, 2(2): 35-40.
[14] Choudhury, A., Medioni. G.: "A framework for Robust Online Video Contrast Enhancement Using Modularity Optimization," Circuits and Systems for Video Technology, IEEE Transactions on, 2012, (22), 9, pp. 1266 – 1279.
[15] B.H. Shekar and K.P. Uma, “Kirsch directional derivatives based shot boundary detection: an efficient and accurate method”, Procedia Computer Science, pp. 565-571, 2015.
[16] Sowmyayani. S., Arockia Jansi Rani, P., “Block based Motion Estimation using Octagon and Square Pattern”, Int. Journal of Signal Processing., Image Processing and Pattern Recognition, vol. 7, no. 4, pp.317-324, 2014.
[17] Kavitha. J, Sowmyayani. S., Arockia Jansi Rani. P., “Shot Boundary Detection Using DWT and Texture Features”, IEEE International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16).
[18] Open Video Project (OVP) (online) http://www.open-video.org/.
[19] TRECVID Dataset (online) http://trecvid.nist.gov/.