Abstract
Ideal video fingerprinting should be robust to various practical distortions. Conventional fingerprinting mainly copes with natural distortions (brightness change, resolution reduction, etc.), while always gives poor performance in case of text insertion. One alterative way is to apply a weighting scheme based on the probability of text insertion for feature similarity calculation. However, the weights must be learned with labeled samples. In this paper, we propose a method that first addresses valid regions where the saliency values keep consistent between the query and original frames, namely saliency-consistent regions. Other regions, probably the inserted ones, are discarded. Then a DCT-based hamming distance is calculated on those saliency-consistent regions. Besides, the saliency-based distance is also considered and a further weighted linear distance is evaluated. The proposed algorithm is tested on the MPEG-7 video fingerprint dataset, achieving a false rate of 0.7% in case of text insertion and 0.32% in average for other 8 distortions.
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Chen, L., Stentiford, F.W.M.: Video Sequence Matching based on Temporal Ordinal Measurement. Pattern Recognition Letters 29, 1824–1831 (2008)
Coskun, B., Sankur, B., Memon, N.: Spatio-Temporal Transform Based Video Hashing. IEEE Transactions on Multimedia 8, 1190–1208 (2006)
Mohan, R.: Video Sequence Matching. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 6, pp. 3697–3700 (1998)
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. 117–128. Springer, Heidelberg (2002)
Lee, S., Yoo, C.D.: Robust Video Fingerprinting for Content-based Video Identification. IEEE Trans. Circuits Syst. Video Technol. 18, 983–988 (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, vol. 6820 (2008)
Law-To, J., Buisson, O., Gouet-Brunet, V., Boujemaa, N.: Robust Voting Algorithm based on Labels of Behavior for Video Copy Detection. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, Santa Barbara (2006)
Iwamoto, K., Kasutani, E., Yamada, A.: Image Signature Robust to Caption Superimposition for Video Sequence Identification. In: International Conference on Image Processing, pp. 3185–3188 (2006)
Kim, C.: Content-based Image Copy Detection. Signal Processing: Image Communication 18, 169–184 (2003)
Itti, L., Koch, C., Niebur, E.: A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Patt. Anal. Mach. Intell., 1254–1259 (1998)
Bober, M., Brasnett, P., Iwamoto, K.: Description of Core Experiment for MPEG-7 Visual Descriptors, http://www.chiariglione.org/mpeg/working_documents/mpeg-07/visual/visual_ce.zip
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Yang, R., Tian, Y., Huang, T. (2009). DCT-Based Videoprinting on Saliency-Consistent Regions for Detecting Video Copies with Text Insertion. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_70
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DOI: https://doi.org/10.1007/978-3-642-10467-1_70
Publisher Name: Springer, Berlin, Heidelberg
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