An Efficient Shot Boundary Detection Using Data-cube Searching Technique

Author(s): J. Kavitha*, P. Arockia Jansi Rani, P. Mohamed Fathimal, Asha Paul

Journal Name: Recent Advances in Computer Science and Communications
Formerly Recent Patents on Computer Science

Volume 13 , Issue 4 , 2020


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Background: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization.

Objective: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file.

Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition.

Results: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate.

Conclusion: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.

Keywords: Shot boundary detection, video summarization, content-based video retrieval, abrupt transition detection, and gradual transition detection, frame features.

[1]
M. Asha Paul, J. Kavitha, and P. Arockia Jansi Rani, "Key-frame extraction techniques- A review", Recent Pat. Comput. Sci., vol. 11, p. 3, 2018.
[http://dx.doi.org/10.2174/2213275911666180719111118]
[2]
B-L. Yeo, and B. Liu, "Rapid scene analysis on compressed video", IEEE Trans. Circ. Syst. Video Tech., vol. 5, no. 6, pp. 533-544, 1995.
[3]
T. Barbu, "Novel automatic video cut detection techniques using Gabor filtering", Comput. Electr. Eng., vol. 35, no. 5, pp. 712-721, 2009.
[http://dx.doi.org/10.1016/j.compeleceng.2009.02.003]
[4]
R. Lienhart, "Reliable transition detection in videos: A survey and practitioner’s guide", Int. J. Image Graph., vol. 1, no. 3, pp. 469-486, 2001.
[http://dx.doi.org/10.1142/S021946780100027X]
[5]
I. Koprinska, and S. Carrato, "Temporal video segmentation: A survey", Signal Process. Image Commun., vol. 16, no. 5, pp. 477-500, 2001.
[http://dx.doi.org/10.1016/S0923-5965(00)00011-4]
[6]
A. Hanjalic, "Shot boundary detection: Unraveled and resolved?", IEEE Trans. Circ. Syst. Video Tech., vol. 12, no. 2, pp. 90-105, 2002.
[http://dx.doi.org/10.1109/76.988656]
[7]
E. Ardizzone, and M. Cascia, "Automatic video database indexing and retrieval", Multimedia Tools Appl., vol. 4, pp. 29-56, 1997.
[http://dx.doi.org/10.1023/A:1009630331620]
[8]
A. Nagasaka, and Y. Tanaka, "Automatic video indexing and full-video search for object appearances", Proc. IFIP TC2/WG 2.6 Second Working Conf. on Visual Database Systems II, , 1992pp. 113-127
[9]
H.J. Zhang, A. Kankanhalli, and S.W. Smoliar, "Automatic partitioning of full-motion video", Multimedia Syst., vol. 1, no. 1, pp. 10-28, 1993.
[http://dx.doi.org/10.1007/BF01210504]
[10]
R. Kasturi, and R. Jain, Dynamic Vision, Computer Vision: Principles., IEEE Computer Society Press: Washington, DC, 1991, pp. 469-480.
[11]
B. Shahraray, "Scene change detection and content-based sampling of video sequences", Proc. SPIE, vol. 2419, pp. 2-13, 1995.
[http://dx.doi.org/10.1117/12.206348]
[12]
W. Xiong, and J.C-M. Lee, "Efficient scene change detection and camera motion annotation for video classification", Comput. Vis. Image Underst., vol. 71, no. 2, pp. 166-181, 1998.
[13]
J. Yu, and M.D. Srinath, "An efficient method for scene cut detection", Pattern Recognit. Lett., vol. 22, no. 13, pp. 1379-1391, 2001.
[http://dx.doi.org/10.1016/S0167-8655(01)00085-X]
[14]
J. Mas, and G. Fernandez, "Video Shot Boundary Detection based on Colour Histogram",
[15]
D. Swanberg, C. Shu, and R. Jain, "Knowledge-guided parsing in video databases", Proceedings of SPIE Symposium on Electronic Imaging: Science and Technology, 1993pp. 13-24
[16]
C.M. Lee, and M.C. Ip, "A Robust Approach for Camera Break Detection in color Video Sequence", Proc. IAPR Workshop on Machine Vision Application (MVA,94), 1994 Kawasaki, Japan
[17]
H-W. Yoo, H-J. Ryoo, and D-S. Jang, "Gradual shot boundary detection using localized edge blocks", Multimed. Tools, vol. 28, no. 3, pp. 283-300, 2006.
[18]
A.M. Ferman, and A.M. Tekalp, "Efficient filtering and clustering methods for temporal video representation and visual summarization", J. Visual Com. & Image Rep., vol. 9, pp. 336-351, 1998.
[http://dx.doi.org/10.1006/jvci.1998.0402]
[19]
A. Hampapur, R. Jain, and T.E. Weymouth, "Production model based digital video segmentation", Multimedia Tools Appl., vol. 1, no. 1, pp. 9-46, 1995.
[http://dx.doi.org/10.1007/BF01261224]
[20]
F. Arman, A. Hsu, and M-Y. Chiu, "Image processing on compressed data for large video databases", MULTIMEDIA ’93: Proceedings of the first ACM international conference on Multimedia, 1993pp. 267-272
[21]
J-C. Ren, Determination of shot boundary in MPEG videos for TRECVID 2007
[22]
Y. Kawai, H. Sumiyoshi, and N. Yagi, "Shot boundary detection at TRECVID 2007", Proc. TREC Video Retr. Eval. Online, 2007
[23]
J. Li, Y. Ding, Y. Shi, and W. Li, "A divide-and-rule scheme for shot boundary detection based on sift", J. Digital Content Tech. Appl., vol. 4, pp. 202-214, 2010.
[24]
S. Lian, "Automatic video temporal segmentation based on multiple features", Soft Comput., vol. 15, no. 3, pp. 469-482, 2011.
[http://dx.doi.org/10.1007/s00500-009-0527-9]
[25]
https://en.wikipedia.org/wiki/YUV
[26]
H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-up robust features (SURF)", Comput. Vis. Image Underst., vol. 110, no. 3, pp. 346-359, 2008.
[http://dx.doi.org/10.1016/j.cviu.2007.09.014]
[27]
J. Kavitha, and S. Sowmyayani, "Arockia Jansi Rani P. “Wavelet-based feature vector for shot boundary detection", Int. J. Image Graph., vol. 17, no. 1, 2017.1750002
[28]
TRECVid Dataset, trecvid.nist.gov
[29]
www.open-video.org
[30]
G.G. Lakshmi Priya, and S. Domnic, "Walsh–hadamard transform kernel-based feature vector for shot boundary detection", IEEE Trans. Image Process., vol. 23, no. 12, pp. 5187-5197, 2014.


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 13
ISSUE: 4
Year: 2020
Published on: 18 October, 2020
Page: [798 - 807]
Pages: 10
DOI: 10.2174/2213275912666190830141628
Price: $25

Article Metrics

PDF: 13
HTML: 1