short-paper

Abrupt Shot Detection in Video using Weighted Edge Information

Authors:

H. S. NagendraswamyAuthors Info & Claims

ICIA-16: Proceedings of the International Conference on Informatics and Analytics

Article No.: 69, Pages 1 - 5

https://doi.org/10.1145/2980258.2980406

Published: 25 August 2016 Publication History

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Abstract

Content Based Video Retrieval (CBVR) has been extensively utilized for automatic indexing, retrieval and management of video data. Segmentation of video is the prominent step in Content Based Video Retrieval. In this paper, we focus on automatic detection of abrupt shot cuts in video sequences. The proposed approach exploits the edge information of an image of a video frame for its characterization. A (2x2) mask of sliding window is used in both overlapping and non overlapping mode to assign binary weights to the edge information of an image. The binary weights evaluated for each mask is used to construct histogram for each image which forms the feature vector to represent an image. The Euclidean distance between the feature vectors of adjacent frames of a video are computed and these values are used for shot cut detection process using adaptive thresholding. To check the efficacy of the proposed shot boundary detection approach, experiments were carried out on a subset of standard video data set TRECVID 2001. The experimental results obtained by the proposed algorithm outperform some of the existing shot boundary detection algorithms in terms of precision, recall and F-measure rates.

References

[1]

Nagasaka, A. and Tanaka, Y., 1992. Automatic video indexing and full-video search for object appearances. In Visual Database Systems II. Elsevier Science Publisher. (1992), 113--117.

Digital Library

[2]

Hu, W., Xie, N., Li, L., Zeng, X. and Maybank, S., 2011. A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 41, 6 (2011), 797--819.

Digital Library

[3]

Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F. and Zhang, B., 2007. A formal study of shot boundary detection. IEEE transactions on circuits and systems for video technology. 17, 2 (2007), 168--186.

Digital Library

[4]

Zhang, H., Kankanhalli, A. and Smoliar, S.W., 1993. Automatic partitioning of full-motion video. Multimedia systems. 1,1(1993), 10--28.

Digital Library

[5]

Kasturi, R., Strayer, S.H., Gargi, U. and Antani, S., 1996. An evaluation of color histogram based methods in video indexing. In International workshop on image database and multi media search, Amsterdam, The Netherlands. (August. 1996), 75--82.

[6]

Adjeroh, D.A. and Lee, M.C., 1997. Robust and efficient transform domain video sequence analysis: An approach from the generalized color ratio model. Journal of Visual Communication and Image Representation. 8,2 (1997), 182--207.

Digital Library

[7]

Kasturi, R., and Jain, R. C. 1991. Dynamic vision. In: Kasturi R, Jain RC, editors. Computer vision: principles. Washington, DC: IEEE Computer Society Press. (1991), 469--80.

[8]

Swanberg, D., Shu, C.F. and Jain, R.C., 1993. Knowledge-guided parsing in video databases. In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology. (April. 1993). 13--24. International Society for Optics and Photonics.

[9]

Courtney, J.D., 1997. Automatic video indexing via object motion analysis. Pattern Recognition. 30, 4 (1997), 607--625.

[10]

Bouthemy, P., Gelgon, M. and Ganansia, F., 1999. A unified approach to shot change detection and camera motion characterization. IEEE Transactions on Circuits and Systems for Video Technology. 9, 7 (1999), 1030--1044.

Digital Library

[11]

Priya, G.L. and Domnic, S., 2012. Edge strength extraction using orthogonal vectors for shot boundary detection. Procedia Technology. 6 (2012), 247--254.

[12]

Shekar, B.H. and Uma, K.P., 2015. Kirsch Directional Derivatives Based Shot Boundary Detection: An Efficient and Accurate Method. Procedia Computer Science. 58 (2015), 565--571.

[13]

Manjunath, S., Guru, D.S., Suraj, M.G. and Harish, B.S., 2011, March. A non parametric shot boundary detection: an eigen gap based approach. In Proceedings of the Fourth Annual ACM Bangalore Conference. (2011), 14.

Digital Library

[14]

Song, S.M., Kwon, T.H., Kim, W.M., Kim, H. and Rhee, B.D., 1997. Detection of gradual scene changes for parsing of video data. In Photonics West'98 Electronic Imaging. (Dec. 1997), 404--413. International Society for Optics and Photonics.

[15]

Hoi, S.C., Wong, L.L. and Lyu, A., 2006. Chinese university of hongkong at trecvid 2006: Shot boundary detection and video search. In TRECVid 2006 Workshop. (2006), 76--86.

[16]

Manjunath, B.S. and Ma, W.Y., 1996. Texture features for browsing and retrieval of image data. IEEE Transactions on pattern analysis and machine intelligence. 18, 8 (1996), 837--842.

Digital Library

[17]

Liu, Y., Chen, X., Yao, H., Cui, X., Liu, C. and Gao, W., 2009. Contour-motion feature (CMF): A space-time approach for robust pedestrian detection. Pattern Recognition Letters. 30, 2 (2009), 148--156.

Digital Library

[18]

Hauptmann, A., Baron, R.V., Chen, M.Y., Christel, M., Duygulu, P., Huang, C., Jin, R., Lin, W.H., Ng, T. and Moraveji, N., 2004. Informedia at TRECVID 2003: Analyzing and searching broadcast news video. CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE.

[19]

Zabih, R., Miller, J. and Mai, K., 1999. A feature-based algorithm for detecting and classifying production effects. Multimedia systems, 7, 2 (1999), 119--128.

Digital Library

[20]

Abdesselam, A., 2013. Improving local binary patterns techniques by using edge information. Lecture Notes on Software Engineering. 1, 4 (2013), 360.

[21]

Yao, C.H. and Chen, S.Y., 2003. Retrieval of translated, rotated and scaled color textures. Pattern Recognition. 36, 4 (2003) 913--929.

[22]

Ford, R.M., Robson, C., Temple, D. and Gerlach, M., 2000. Metrics for shot boundary detection in digital video sequences. Multimedia Systems. 8, 1 (2000), 37--46.

Digital Library

[23]

Dugad, R., Ratakonda, K. and Ahuja, N., 1998. Robust video shot change detection. In Multimedia Signal Processing. (Dec. 1998), 376--381.

[24]

Adjeroh, D., Lee, M.C., Banda, N. and Kandaswamy, U., 2009. Adaptive edge-oriented shot boundary detection. EURASIP Journal on Image and Video Processing. 2009, 1 (2009), 1.

Digital Library

[25]

Li, W.K. and Lai, S.H., 2003. Integrated video shot segmentation algorithm. In Electronic Imaging. (Jan. 2003), 264--271. International Society for Optics and Photonics.

Cited By

Rashmi BNagendraswamy H(2021)Video shot boundary detection using block based cumulative approachMultimedia Tools and Applications10.1007/s11042-020-09697-680:1(641-664)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s11042-020-09697-6
Mahrishi MMorwal S(2020)Index Point Detection and Semantic Indexing of Videos—A Comparative ReviewSoft Computing: Theories and Applications10.1007/978-981-15-4032-5_94(1059-1070)Online publication date: 30-Jun-2020
https://doi.org/10.1007/978-981-15-4032-5_94
Rashmi B S Nagendraswamy H S (2018)Effective Video Shot Boundary Detection and Keyframe Selection using Soft Computing TechniquesInternational Journal of Computer Vision and Image Processing10.4018/IJCVIP.20180401028:2(27-48)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.4018/IJCVIP.2018040102
Show More Cited By

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cover image ACM Other conferences

ICIA-16: Proceedings of the International Conference on Informatics and Analytics

August 2016

868 pages

ISBN:9781450347563

DOI:10.1145/2980258

Copyright © 2016 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2016

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ICIA-16: International Conference on Informatics and Analytics

August 25 - 26, 2016

Pondicherry, India

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Cited By

Rashmi BNagendraswamy H(2021)Video shot boundary detection using block based cumulative approachMultimedia Tools and Applications10.1007/s11042-020-09697-680:1(641-664)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s11042-020-09697-6
Mahrishi MMorwal S(2020)Index Point Detection and Semantic Indexing of Videos—A Comparative ReviewSoft Computing: Theories and Applications10.1007/978-981-15-4032-5_94(1059-1070)Online publication date: 30-Jun-2020
https://doi.org/10.1007/978-981-15-4032-5_94
Rashmi B S Nagendraswamy H S (2018)Effective Video Shot Boundary Detection and Keyframe Selection using Soft Computing TechniquesInternational Journal of Computer Vision and Image Processing10.4018/IJCVIP.20180401028:2(27-48)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.4018/IJCVIP.2018040102
Yu JLei ASong ZWang TCai HFeng N(2018)Comprehensive Dataset of Broadcast Soccer Videos2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00090(418-423)Online publication date: Apr-2018
https://doi.org/10.1109/MIPR.2018.00090
Bi CYuan YZhang JShi YXiang YWang YZhang R(2018)Dynamic Mode Decomposition Based Video Shot DetectionIEEE Access10.1109/ACCESS.2018.28251066(21397-21407)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2825106

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