CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Robust Video Copy Detection System using TIRI-DCT and DWT Fingerprints

by Devi.s, N. Vishwanath, S. Muthu Perumal Pillai
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 6
Year of Publication: 2012
Authors: Devi.s, N. Vishwanath, S. Muthu Perumal Pillai
10.5120/8048-1382

Devi.s, N. Vishwanath, S. Muthu Perumal Pillai . A Robust Video Copy Detection System using TIRI-DCT and DWT Fingerprints. International Journal of Computer Applications. 51, 6 ( August 2012), 29-34. DOI=10.5120/8048-1382

@article{ 10.5120/8048-1382,
author = { Devi.s, N. Vishwanath, S. Muthu Perumal Pillai },
title = { A Robust Video Copy Detection System using TIRI-DCT and DWT Fingerprints },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 6 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number6/8048-1382/ },
doi = { 10.5120/8048-1382 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:08.907508+05:30
%A Devi.s
%A N. Vishwanath
%A S. Muthu Perumal Pillai
%T A Robust Video Copy Detection System using TIRI-DCT and DWT Fingerprints
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 6
%P 29-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A video copy detection system that is based on content fingerprinting can be used for video indexing and copyright applications. Most of the video copy detection algorithms proposed so far focus mostly on coping with signal distortions introduced by different encoding parameters; however, these algorithms do not cope well with display format conversions. They may rely on a fingerprint extraction algorithm followed by a fast approximate search algorithm. The fingerprint extraction algorithm extracts compact content-based signatures [11]-[14] from special images constructed from the video. Each such image represents a short segment of the video and contains temporal as well as spatial information [7], [8], [10] about the video segment. These images are denoted by temporally informative representative images [1]. To find whether a query video is copied from a video in a video database, the fingerprints of all the videos in the database are extracted and stored in advance. The search algorithm searches the stored fingerprints to find close enough matches for the fingerprints of the query video. The content based fingerprint extraction process does not work to get a better level search. Further in an enhancement of this concept of fingerprints this system handles the TIRI-DCT and DWT features to detect the copyright information. This paper proposes a novel sequence matching technique to detect copies of a video clip. If a video copy detection technique is to be effective, it needs to be robust to the many digitization and encoding processes that give rise to several distortions, including changes in brightness, color, frame format, as well as different blocky artifacts. It also handles a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and it is proposed to achieve accurate duplicate detection. Then the performance of the TIRI DCT and the co-efficient is compared. The proposed method has been extensively tested and the results show that the proposed scheme is effective in detecting copies which has been subjected to wide range of modifications.

References
  1. Mani Malek Esmaeili, Mehrdad Fatourechi, and Rabab Kreidieh Ward, "A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting", IEEE Transactions On Information Forensics And Security, Vol. 6, No. 1, March 2011
  2. X. Su, T. Huang, and W. Gao, "Robust video fingerprinting based on visual attention regions," in Proc. ICASSP,Washington, DC, 2009, pp. 1525–1528, IEEE Computer Society.
  3. A. Joly, O. Buisson, and C. Frelicot, "Content-based copy retrieval using distortion-based probabilistic similarity search," IEEE Trans. Multimedia, vol. 9, no. 2, pp. 293–306, Feb. 2007.
  4. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos," in Proc. IEEE Int. Conf. Computer Vision (ICCV), Washington, DC, 2003, p. 1470, IEEE Computer Society.
  5. A. Joly, C. Frélicot, and O. Buisson, "Feature statistical retrieval applied to content-based copy identification," in Proc. Int. Conf. Image Processing, 2004.
  6. A. Swaminathan, Y. Mao, and M. Wu, "Robust and secure image hashing," IEEE Trans. Inf. Forensics Security, vol. 1, no. 2, pp. 215–230, Jun. 2006.
  7. C. Kim and B. Vasudev, "Spatiotemporal sequence matching for efficient video copy detection," IEEE Trans. Circuits Syst. Video Technol. , vol. 15, no. 1, pp. 127–132, Jan. 2005.
  8. M. Malekesmaeili, M. Fatourechi, and R. K. Ward, "Video copy detection using temporally informative representative images," in Proc. Int. Conf. Machine Learning and Applications, Dec. 2009, pp. 69–74.
  9. M. Malekesmaeili and R. K. Ward, "Robust video hashing based on temporally informative representative images," in Proc. IEEE Int. Conf. Consumer Electronics, Jan. 2010, pp. 179–180.
  10. B. Coskun, B. Sankur, and N. Memon, "Spatiotemporal transform based video hashing," IEEE Trans. Multimedia, vol. 8, no. 6, pp. 1190–1208, Dec. 2006.
  11. Yu Xiaohong, Xu Jinhua, "The Related Techniques of Content-based Image Retrieval", In International Symposium on Computer Science and Computational Technology, 2008.
  12. Pengyu Liu, Kebin Jia, Zhuozheng Wang, Zhuoyi Lv, "A New and Effective Image Retrieval Method Based on Combined Features", In Fourth International Conference on Image and Graphics,2007.
  13. H. B. Kekre, Sudeep D. Thepade, "Rendering Futuristic Image Retrieval System", In Proc. of National Conference EC2IT-2009, KJSCOE, Mumbai, 20-21 Mar 2009.
  14. Dr. N. Krishnan, M. Sheerin Banu, C. Callins Christiyana, "Content Based Image Retrieval using Dominant Colour Identification Based on Foreground Objects", In International Conference on Computational Intelligence and Multimedia Applications, 2007
  15. H. B. Kekre, Sudeep D. Thepade, "Improving the Performance of Image Retrieval using Partial Coefficients of Transformed Image", International Journal of Information Retrieval, Serials Publications, Volume 2, Issue 1, 2009, pp. 72-79.
  16. S. -C. Cheung and A. Zakhor, "Fast similarity search and clustering of video sequences on the world-wide-web," IEEE Trans. Multimedia, vol. 7, no. 3, pp. 524–537, Jun. 2004.
  17. S. S. Cheung and A. Zakhor, "Efficient Video Similarity Measurement with Video Signature," IEEE Trans. Circuits and Systems for Video Technology, vol. 13, no. 1, pp. 59-74, Jan. 2003.
  18. J. Oostveen, T. Kalker, and J. Haitsma, "Feature extraction and a database strategy for video fingerprinting," in Proc. Int. Conf. Recent Advances in Visual Information Systems (VISUAL), London, U. K. , 2002,pp. 117–128, Springer-Verlag.
  19. G. Langelaar, I. Setyawan, R. Langedijk, Watermarking digital image and video data. A state-of-the-art overview, IEEE Signal Processing Magazine 17 (5) (2000) 20–46.
Index Terms

Computer Science
Information Sciences

Keywords

Content-based Fingerprints Feature extraction Copyright protection video copy detection video matching. Clusters. Fingerprints

Powered by PhDFocusTM