A Video Copy Detection System based on Human Visual System

Yu Bai, Li Zhuo, YingDi Zhao, Xiaoqin Song

2013

Abstract

The technology of near-duplicate video detection is currently a research hot spot in the field of multimedia information processing. It has great value in the areas such as large scale video information indexing and copyright protection. In the case of large-scale data, it is very important to ensure the accuracy of detection and robustness, in the meanwhile improving the processing speed of video copy detection. In this respect, a HVS(Human Visual System)-based video copy detection system is proposed in this paper.This system utilizes the visual attention model to extract the region of interest(ROI) in keyframes, which extracts the Surfgram feature only from the information in ROI, rather than all of the information in the keyframe, thus effectively reducing the amount of the data to process. The experimental results have shown that the proposed algorithm can effectively improve the speed of detection and perform good robustness against brightness changes, contrast changes, frame drops and Gaussian noise.

References

  1. Meng Wang., 2009. Unified Video Annotation Via MultiGraph Learning. IEEE Transactions on Circuits and Systems for Video Technology.
  2. Wu Ming-Ni., Lin Chia-Chen., Chang Chin-Chen., 2006. A Robust Content based Copy Detection Scheem. Fundamenta Informaticae.
  3. Kim, C., 2003. Content-based Image Copy Detection. Singal Processing: Image Communication.
  4. M, Douze., A, Gaidon., H, Jegon., M, Marszatke., and C, Schmid., 2008. Inria-Learrs Video copy detection system. In TRECVID.
  5. Y, Hou., H, Z, Hou., 2009. Shot segmentation method based on intensity histogram frame difference. Computer Engineering and Applications.
  6. L, Itti., C, Koch., Emst Niebur., 1998. A Model of Saliency-Based Visual Attention for Rapid Scene.Analysis. IEEE transactions on pattern analysis and machine intelligence.
  7. Bay, H., Tuytelaars, T., Van Gool, L., 2008. Speeded-Up Robust Features(SURF). Computer Vision and Image Understanding.
  8. B, Cui et al., 2005. Exploring bit difference for approximate KNN search in high dimensional databases. Proceedings of the 16th Australasian database conference.
  9. N, Apostol., H, Matthew., S, John R., 2010. Design and evaluation of an effective and efficient video copy detection system. 2010 IEEE International Conference on Multimedia and Expo.
Download


Paper Citation


in Harvard Style

Bai Y., Zhuo L., Zhao Y. and Song X. (2013). A Video Copy Detection System based on Human Visual System . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 792-795. DOI: 10.5220/0004292107920795


in Bibtex Style

@conference{visapp13,
author={Yu Bai and Li Zhuo and YingDi Zhao and Xiaoqin Song},
title={A Video Copy Detection System based on Human Visual System},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={792-795},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004292107920795},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - A Video Copy Detection System based on Human Visual System
SN - 978-989-8565-47-1
AU - Bai Y.
AU - Zhuo L.
AU - Zhao Y.
AU - Song X.
PY - 2013
SP - 792
EP - 795
DO - 10.5220/0004292107920795