Abstract
In this paper, we present a novel approach for video fingerprinting by using boosted Harr-like features and direct hashing. Through employing a pairwise boosting method on a large set of features, our system can learn the top-M discriminative filters that are enable to efficient extracting video fingerprints. During query phase, we retrieve video clips by using a fast and accurate direct hashing, which minimizes perceptual Hamming distance between queries and a large database of pre-computed fingerprints. To demonstrate the superiority of our method, we also implement four other fingerprinting methods for comparisons. The experimental results indicate that our proposed method can significantly outperform those four methods in video retrieval.
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.
References
Oostveen, J., Kalker, T., Haitsma, J.: Feature Extraction and a Database Strategy for Video Fingerprinting. In: Chang, S.-K., Chen, Z., Lee, S.-Y. (eds.) VISUAL 2002. LNCS, vol. 2314, pp. 67–81. Springer, Heidelberg (2002)
Lee, S., Yoo, C.D.: Robust Video Fingerprinting for Content-Based Video Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 707–711 (2002)
Lee, S., Yoo, C.D.: Video Fingerprinting Based on Centroids of Gradient Orientations. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), vol. 2, pp. 14–19 (2006)
Kim, H.S., Lee, J., Liu, H.B., Lee, D.W.: Video Linkage: Group Based Copied Video Detection. In: ACM Int’l Conf. on Image and Video Retrieval (CIVR), Niagara Falls, Canada, July 2008, pp. 397–406 (2008)
Ramachandra, V., Zwicker, M., Nguyen, T.: 3D Video Fingerprinting. In: 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video 2008, pp. 28–30, 81–84 (2008)
Sarkar, A., Ghosh, P., Moxley, E., Manjunath, B.S.: Video Fingerprinting: Features for Duplicate and Similar Video Detection and Query-based Video Retrieval. In: Proc. SPIE - Multimedia Content Access: Algorithms and Systems II, San Jose, California (January 2008)
Oostveen, J., Kalker, T., Haitsma, J.: An efficient Database Search Strategy for Audio Fingerprinting. In: IEEE Workshop on Multimedia Signal Processing 2002, December 9-11, pp. 178–181 (2002)
Ke, Y., Hoiem, D., Sukthankar, R.: Computer Vision for Music Identification. In: Proceedings of Computer Vision and Pattern Recognition 2005, vol. 2, pp. 1184–1192 (2005)
Kim, S., Yoo, C.D.: Boosted Binary Audio Fingerprint Based on Spectral Subband Moments. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007, April 15-20, pp. 241–244 (2007)
Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Proceedings of Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)
Baluja, S., Covell, M.: Audio Fingerprinting: Combining Computer Vision & Data Stream Processing. In: IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2007, April 15-20, pp. 213–216 (2007)
Baluja, S., Covell, M.: Waveprint: Efficient wavelet-based audio fingerprinting. Pattern Recognition 41(11), 3467–3480 (2008)
Schapire, R.E.: A Brief Introduction to Boosting. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence 1999, San Francisco, CA, USA, pp. 1401–1406 (1999)
Indyk, P., Motwani, R.: Approximate Nearest Neighbor- towards Removing the Curse of Dimensionality. In: Proceedings of Symposium on Theory of Computing, pp. 604–613 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lian, H., Xu, J. (2009). Video Fingerprinting by Using Boosted Features. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-01513-7_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
Online ISBN: 978-3-642-01513-7
eBook Packages: Computer ScienceComputer Science (R0)