Abstract
Large scale near-duplicate video detection is very desirable for web video processing, especially the computational efficiency is essential for practical applications. In this paper, we present a computationally efficient algorithm based on multi-layer video content analysis. Local features are extracted from key frames of videos and indexed by an novel adaptive locality sensitive hashing scheme. By learning several parameters, fast retrieval in the new hashing structure is performed without high dimensional distance computations and achieves better real-time retrieving performance compared with other state-of-the-art approaches. Then a descriptor filtering method and a two-level matching scheme is performed to generate a relevance score for detection. Experiments on near-duplicate video detection tasks including various transformed videos demonstrate the efficiency gains of the proposed algorithm.
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Zhang, Z., Cao, C., Zhang, R., Zou, J.: Video Copy Detection Based on Speeded Up Robust Features and Locality Sensitive Hashing. In: Proc. IEEE Int. Conf. Automation and Logistics, pp. 13–18 (2010)
Bay, H., Tuytelaars, T., Van Gool, L.: Speeded-up Robust Features (SURF). Comput. Vis. Image Underst. 3(110), 404–417 (2008)
Gionis, A., Indyk, P., Motwani, R.: Similarity Search in High Dimensions via Hashing. In: Proc. Int. Conf. Very Large Data Bases, pp. 518–529 (1999)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.: Locality-sensitive hashing Scheme Based on p-stable Distributions. In: Proc. ACM Symposium on Computational Geometry (2004)
Yeh, M., Cheng, K.-T.: Fast Visual Retrieval Using Accelerated Sequence Matching. IEEE Trans. Multimedia 13(2), 320–329 (2011)
Yeh, M., Cheng, K.T.: A Compact, Effective Descriptor for Video Copy Detection. In: Proc. ACM Int. Conf. Multimedia, pp. 633–636 (2009)
Caspi, Y., Irani, M.: Spatio-Temporal Alignment of Sequences. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1409–1424 (2002)
Shang, L., Yang, L., Wang, F., Chan, K., Hua, X.: Real-time Large Scale Near-duplicate Web Video Retrieval. In: Proc. ACM Int. Conf. Multimedia, pp. 531–540 (2010)
Liu, X., Liu, T., Gibbon, D., Shahraray, B.: Effective and Scalable Video Copy Detection. In: Proc. ACM Int. Conf. Multimedia Information Retrieval, pp. 119–128 (2010)
Law-To, J., Chen, L., Joly, A., Laptev, I., Buisson, O., Gouet-Brunet, V., Boujemaa, N., Stentiford, F.: Video Copy Detection: A Comparative Study. In: Proc. ACM Int. Conf. Image and Video Retrieval (2007)
Kim, C., Vasudev, B.: Spatiotemporal Sequence Matching for Efficient Video Copy Detection. IEEE Trans. Circuits Syst. Video Technol. 15(1), 127–132 (2005)
Avrithis, Y., Tolias, G., Kalantidis, Y.: Feature Map Hashing: Sub-linear Indexing of Appearance and Global Geometry. In: Proc. ACM Int. Conf. Multimedia, pp. 231–240 (2010)
Chiu, C., Wang, H., Chen, C.: Fast Min-hashing Indexing and Robust Spatio-temporal Matching for Detection Video Copies. ACM Trans. Multimed. Comput. Comm. Appl. 6(2), Article 10 (2010)
Poullot, S., Buisson, O., Crucianu, M.: Scaling Content-based Video Copy Detection to Very Large Databases. Multimed. Tools Appl. 47, 279–306 (2010)
Law-To, J., Joly, A., Boujemaa, N.: Muscle-VCD-2007: A Live Benchmark for Video Copy Detection (2007)
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Liu, D., Yu, Z. (2015). A Computationally Efficient Algorithm for Large Scale Near-Duplicate Video Detection. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8936. Springer, Cham. https://doi.org/10.1007/978-3-319-14442-9_53
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DOI: https://doi.org/10.1007/978-3-319-14442-9_53
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