ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

A Frame-Based Video Signature Method for Very Quick Video Identification and Location

  • Received : 2012.05.08
  • Accepted : 2012.10.12
  • Published : 2013.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

A video signature is a set of feature vectors that compactly represents and uniquely characterizes one video clip from another for fast matching. To find a short duplicated region, the video signature must be robust against common video modifications and have a high discriminability. The matching method must be fast and be successful at finding locations. In this paper, a frame-based video signature that uses the spatial information and a two-stage matching method is presented. The proposed method is pair-wise independent and is robust against common video modifications. The proposed two-stage matching method is fast and works very well in finding locations. In addition, the proposed matching structure and strategy can distinguish a case in which a part of the query video matches a part of the target video. The proposed method is verified using video modified by the VCE7 experimental conditions found in MPEG-7. The proposed video signature method achieves a robustness of 88.7% under an independence condition of 5 parts per million with over 1,000 clips being matched per second.

Keywords

References

  1. Wikipedia. http://en.wikipedia.org/wiki/Youtube
  2. T. Kalker, J.A. Haitsma, and J. Oostveen, "Issues with Digital Watermarking and Perceptual Hashing," Proc. SPIE, Multimedia Syst. Appl. IV, Nov. 2001, pp. 189-197.
  3. J.S. Seo et al., "Audio Fingerprinting Based on Normalized Spectral Subband Moments," IEEE Signal Process. Lett., vol. 13, no. 4, Apr. 2006, pp. 209-212. https://doi.org/10.1109/LSP.2005.863678
  4. S.M. Kim, S.J. Park, and C.S. Won, "Image Retrieval via Queryby- Layout Using MPEG-7 Visual Descriptors," ETRI J., vol. 29, no. 2, Apr. 2007, pp. 246-248. https://doi.org/10.4218/etrij.07.0206.0177
  5. S.S. Cheung and A. Zakhor, "Efficient Video Similarity Measurement with Video Signature," IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 1, Jan. 2003, pp. 59-74. https://doi.org/10.1109/TCSVT.2002.808080
  6. C. Kim and B. Vasudev, "Spatiotemporal Sequence Matching for Efficient Video Copy Detection," IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 1, Jan. 2005, pp. 127-132. https://doi.org/10.1109/TCSVT.2004.836751
  7. J. Oostveen, T. Kalker, and J. Haitsma, "Feature Extraction and a Database Strategy for Video Fingerprinting," Proc. Int. Conf. Recent Adv. Visual Inf. Syst., 2002, pp. 117-128.
  8. X. Hua, X. Chen, and H. Zhang, "Robust Video Signature Based on Ordinal Measure," Proc. Int. Conf. Image Process., Singapore, Oct. 24-27, 2004, pp. 685-688.
  9. R. Mohan, "Video Sequence Matching," Proc. Int. Conf. Audio, Speech, Signal Process., IEEE Signal Processing Society, vol. 6, 1998, pp. 3697-3700.
  10. A. Hampapur and R.M. Bolle, "VideoGREP: Video Copy Detection Using Inverted File Indices," Technical Report, IBM Research, 2001.
  11. S. Lee and C.D. Yoo, "Robust Video Fingerprinting for Content- Based Video Identification," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 7, July 2008, pp. 983-988. https://doi.org/10.1109/TCSVT.2008.920739
  12. M.-C. Yeh and K.-T. Cheng, "Video Copy Detection by Fast Sequence Matching," Proc. ACM Int. Conf. Image Video Retrieval, Apr. 2009.
  13. MPEG Video Sub-Group, "Description of Core Experiments in Video Signature Description Development," ISO/IEC JTC1/SC29/WG11 , w10345, Lausanne, Switzerland, Feb. 2009.
  14. J.S. Seo et al., "A Robust Image Fingerprinting System Using the Radon Transform," Signal Process.: Image Commun., vol. 19, 2004, pp. 325-339. https://doi.org/10.1016/j.image.2003.12.001
  15. C. Kim, "Content-Based Image Copy Detection," Signal Process.: Image Commun., vol. 18, no. 3, Mar. 2003, pp. 169- 184. https://doi.org/10.1016/S0923-5965(02)00130-3
  16. P. Sebastian and H. Kalva, "Accuracy and Stability Improvement of Tomography Video Signatures," IEEE Int. Conf. Multimedia and Expo, July 2010, pp. 133-137.

Cited by

  1. Adaptive Image Matching Using Discrimination of Deformable Objects vol.8, pp.7, 2013, https://doi.org/10.3390/sym8070068
  2. Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering vol.9, pp.2, 2013, https://doi.org/10.3390/sym9020025
  3. An Integrated Signature-Based Framework for Efficient Visual Similarity Detection and Measurement in Video Shots vol.36, pp.4, 2013, https://doi.org/10.1145/3190784
  4. Research on design and implementation of pedestrian red light automatic early warning system based on face recognition vol.19, pp.None, 2013, https://doi.org/10.3233/jcm-191034