Abstract
Video copy detection is an active research field in copyright control, business intelligence and advertisement monitor etc. The main issues are transformation-invariant feature extraction and robust registration in object level. This paper proposes a novel video copy detection approach based on spatial-temporal-scale registration. In detail, we first build interesting points’ trajectories by speeded up robust features (SURF). Then we use an efficient voting based spatial-temporal-scale registration approach to estimate the optimal transformation parameters and achieve the final video copy detection results by propagations of video segments in both spatial-temporal and scale directions. To speed up the detection speed, we use local sensitive hash indexing (LSH) to index trajectories for fast queries of candidate trajectories. Compared with existing approaches, our approach can detect many kinds of copy transformations including cropping, zoom in/out, camcording and re-encoding etc. Extensive experiments on 200 hours of videos demonstrate the effectiveness of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Duygulu, P., Chen, M., Hauptmann, A.: Comparison and combination of two novel commercial detection methods. In: Proc. CIVR 2004 (July 2004)
Yuan, J., Duan, L., Tian, Q., Xu, C.: Fast and robust short video clip search using an index structure. In: Proc. ACM MIR 2004 (2004)
Law-To, J., Buisson, O., Gouet-Brunet, V., Boujemaa, N.: Robust voting algorithm based on labels of behavior for video copy detection. In: International conference on Multimedia (2006)
Gauch, J., Shivadas, A.: Identification of new commercials using repeated video sequence detection. In: Proc. ICIP 2005, pp. 1252–1255 (2005)
TRECVID2008, http://www-nlpir.nist.gov/projects/tv2008/tv2008.html
Harris, C., Stevens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conference, pp. 153–158 (1988)
Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: International Conference on Computer Vision, pp. 1150–1157 (1999)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: International Conference on Pattern Recognition (2003)
Bay, H., Tuytelaars, T., Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Andoni, A., Indyk, P.: E2LSH0.1 User manual (June 2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, S., Wang, T., Wang, J., Li, J., Zhang, Y., Lu, H. (2008). A Spatial-Temporal-Scale Registration Approach for Video Copy Detection. In: Huang, YM.R., et al. Advances in Multimedia Information Processing - PCM 2008. PCM 2008. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89796-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-89796-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89795-8
Online ISBN: 978-3-540-89796-5
eBook Packages: Computer ScienceComputer Science (R0)