Abstract
The detection of Near-Duplicate Video has been studied by many scholars in recent years. This paper proposes a method for classifying and identifying infringing video by extracting video visual features, obtaining inter-frame relationship graphs, and using CNN classification network. We apply the improved weighting technique of text mining to video solution and still achieve better results. This method is robust in experiments and does not require retraining of a model in practical applications. At the same time, this method can also be applied to large-scale video retrieval. Our method has an F1 score of 95.74%.
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
Similar content being viewed by others
References
Shih, F.Y.: Digital Watermarking and Steganography: Fundamentals and Techniques. CRC Press, Boca Raton (2017)
Chou, C.L., Chen, H.T., Lee, S.Y.: Pattern-based near-duplicate video retrieval and localization on web-scale videos. IEEE. Trans. Multimedia 17(3), 382–395 (2015)
Chen, F.Q., Zhou, Y.P.: Color feature extraction of Hainan Li brocade image based on RGB and HSV. In: International Computer Conference on Wavelet Active Media Technology & Information Processing, pp. 214–219. IEEE (2016)
Nandagopalan, S., Adiga, B.S., Deepak, N.: A universal model for content-based image retrieval. jiP 2, 3436–3439 (2008)
Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Natsev, A., Hill, M., Smith, J.R.: Design and evaluation of an effective and efficient video copy detection system. In: 2010 IEEE International Conference on Multimedia and Expo, pp. 1352–1358. IEEE (2010)
Yang, G., Chen, N., Jiang, Q.: A robust hashing algorithm based on SURF for video copy detection. Comput. Secur. 31(1), 33–39 (2012)
Qi, Y., Qiu, Y.: SURF and KPCA based image perceptual hashing algorithm. In: SPIE (2016)
Song, J., Yang, Y., Huang, Z., et al.: Effective multiple feature hashing for large-scale near-duplicate video retrieval. IEEE. Trans. Multimedia 15(8), 1997–2008 (2013)
Zhou, X., Zhou, X., Chen, L., et al.: An efficient near-duplicate video shot detection method using shot-based interest points. IEEE. Trans. Multimedia 11(5), 879–891 (2009)
Liu, H., Lu, H., Xue, X.: A segmentation and graph-based video sequence matching method for video copy detection. IEEE. Trans. Knowl. Data Eng. 25(8), 1706–1718 (2013)
Roopalakshmi, R., Reddy, G.R.M.: A novel spatio-temporal registration framework for video copy localization based on multimodal features. Sig. Process. 93(8), 2339–2351 (2013)
Liu, H., Zhao, Q., Wang, H., et al.: An image-based near-duplicate video retrieval and localization using improved edit distance. Multimedia Tools Appl. 76(22), 24435–24456 (2017)
Chiu, C.Y., Tsai, T.H., Liou, Y.C., et al.: Near-Duplicate Subsequence Matching Between the Continuous Stream and Large Video Dataset. IEEE. T. MULTIMEDIA. 16(7), 1952–1962 (2014)
Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2008)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Wu, X., Hauptmann, A.G., Ngo, C.-W.: Practical elimination of near-duplicate from web video search. In: ACM International Conference on Multimedia, pp. 218–227 (2007)
Luan, X., Xie, Y., He, J., et al.: Near-duplicate video detection algorithm based on global GSP feature and local ScSIFT feature fusion. J. Phys. Conf. Ser. 960(1), 012034 (2018)
Acknowledgments
This work was mainly supported by the development of digital copyright protection technology scheme based on digital image tamper identification technology, Key Project of Beijing, China (No. Z181100000618006), and also supported by data-driven security production supervision image construction technology and security management situation research (No. Z181100009018003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ai, X., He, Y., Hu, Y., Tian, W. (2019). Inter-frame Relationship Graph Based Near-Duplicate Video Clip Detection Method. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-9917-6_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9916-9
Online ISBN: 978-981-13-9917-6
eBook Packages: Computer ScienceComputer Science (R0)