Abstract
Near-duplicate video clip(NDVC) detection is a special issue of content-based video search. Identifying the videos derived from the same original source is the primary task of this research. In NDVC detection, an important step is to define an effective similarity measure that captures both frame and sequence information inherent to the video clips. To address this, in this paper, we propose a new similarity measure, named as Video Edit Distance(VED), that adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Visual features contain the discriminative information of each video, and sequence context captures the feature variation of it. To reduce the computation cost of inter-video comparison by VED, we extract key frames from video sequences and map each key frame into one single symbol. Various techniques are proposed to compensate the information loss in the measurement. Experimental results demonstrate that the proposed measure is highly effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chang, S.-F., Chen, W., Meng, H.J., Sundaram, H., Zhong, D.: Videoq: An automated content based video search system using visual cues. In: MM, pp. 313–324 (1997)
Chen, L., Ng, R.: On the marriage of lp-norm and edit distance. In: VLDB, pp. 792–803 (2004)
Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD (2005)
Cheung, S., Zakhor, A.: Efficient video similarity measurement with video signature. IEEE Trans. Circuits Syst. Video Techn. 13(1), 59–74 (2003)
Hoad, T.C., Zobel, J.: Fast video matching with signature alignment. In: MIR, Berkeley, California, pp. 262–269 (2003), doi:10.1145/973264.973304
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999), citeseer.ist.psu.edu/jain99data.html
Lee, S.-L., Chun, S.-J., Kim, D.-H., Lee, J.-H., Chung, C.-W.: Similarity search for multidimensional data sequences. In: ICDE, pp. 599–608 (2000)
Ren, W., Singh, S.: Video sequence matching with spatio-temporal constraints. In: ICPR, pp. 834–837 (2004)
Shen, H.T., Ooi, B.C., Zhou, X., Huang, Z.: Towards effective indexing for very large video sequence database. In: SIGMOD, pp. 730–741 (2005)
Zhou, X., Zhou, X., Shen, H.T.: Efficient similarity search by summarization in large video database. In: ADC, pp. 161–167 (2007)
Zhu, X., Wu, X., Fan, J., Elmagarmid, A.K., Aref, W.G.: Exploring video content structure for hierarchical summarization. Multimedia Syst. 10(2), 98–115 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Zhou, X., Zhou, X., Shen, H.T. (2007). A New Similarity Measure for Near Duplicate Video Clip Detection. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-72524-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
eBook Packages: Computer ScienceComputer Science (R0)