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SCIENTIA SINICA Informationis, Volume 48 , Issue 12 : 1697-1708(2018) https://doi.org/10.1360/N112018-00136

Hierarchical feature fusion hashing for near-duplicate video retrieval

More info
  • ReceivedAug 16, 2018
  • AcceptedOct 9, 2018
  • PublishedDec 12, 2018

Abstract


Funded by

国家自然科学基金项目(61671274)

国家自然科学基金项目(61876098)

国家自然科学基金项目(61573219)

山东省重点研究与开发项目(2017CXGC1504)

山东省高校优势学科人才团队培育计划


References

[1] Song J, Yang Y, Huang Z. Effective Multiple Feature Hashing for Large-Scale Near-Duplicate Video Retrieval. IEEE Trans Multimedia, 2013, 15: 1997-2008 CrossRef Google Scholar

[2] Hao Y, Mu T, Hong R. Stochastic Multiview Hashing for Large-Scale Near-Duplicate Video Retrieval. IEEE Trans Multimedia, 2017, 19: 1-14 CrossRef Google Scholar

[3] Liu H, Zhao Q, Wang H. An image-based near-duplicate video retrieval and localization using improved Edit distance. Multimed Tools Appl, 2017, 76: 24435-24456 CrossRef Google Scholar

[4] Lv J, Wu B, Yang S, et al. Efficient large scale near-duplicate video detection base on spark. In: Proceedings of IEEE International Conference on Big Data, Washington, 2016. 957--962. Google Scholar

[5] Chou C L, Chen H T, Lee S Y. Pattern-Based Near-Duplicate Video Retrieval and Localization on Web-Scale Videos. IEEE Trans Multimedia, 2015, 17: 382-395 CrossRef Google Scholar

[6] Nie X S, Jing W Z, Ma L Y, et al. Two-layer video fingerprinting strategy for near-duplicate video detection. In: Proceedings of IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Hong Kong, 2017. 555--560. Google Scholar

[7] Shen H T, Zhou X F, Huang Z, et al. UQLIPS: a real-time near-duplicate video clip detection system. In: Proceedings of the 33rd International Conference on Very Large Data Bases, Vienna, 2007. 1374--1377. Google Scholar

[8] Wei S, Zhao Y, Zhu C. Frame Fusion for Video Copy Detection. IEEE Trans Circuits Syst Video Technol, 2011, 21: 15-28 CrossRef Google Scholar

[9] Zhao G, Pietikainen M. Dynamic texture recognition using local binary patterns with an application to facial expressions.. IEEE Trans Pattern Anal Mach Intell, 2007, 29: 915-928 CrossRef PubMed Google Scholar

[10] Kordopatis-Zilos G, Papadopoulos S, Patras I, et al. Near-duplicate video retrieval by aggregating intermediate cnn layers. In: Proceedings of International Conference on Multimedia Modeling, Cham, 2017. 251--263. Google Scholar

[11] Cai J J, Merler M, Pankanti S, et al. Heterogeneous semantic level features fusion for action recognition. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, Shanghai, 2015. 307--314. Google Scholar

[12] Nie X, Yin Y, Sun J. Comprehensive Feature-Based Robust Video Fingerprinting Using Tensor Model. IEEE Trans Multimedia, 2017, 19: 785-796 CrossRef Google Scholar

[13] Jiang M L, Tian Y H, Huang T J. Video copy detection using a soft cascade of multimodal features. In: Proceedings of International Conference on Multimedia and Expo, Melbourne, 2012. 374--379. Google Scholar

[14] Nie X S, Liu J, Sun J D. Robust video hashing based on representative-dispersive frames. Sci China Inf Sci, 2013, 56: 1-11 CrossRef Google Scholar

[15] Nie X, Chai Y, Liu J. Spherical torus-based video hashing for near-duplicate video detection. Sci China Inf Sci, 2016, 59: 059101 CrossRef Google Scholar

[16] Lin G S, Shen C H, van den Hengel A, et al. Efficient piecewise training of deep structured models for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016. 3194--3203. Google Scholar

[17] Liu Z W, Li X X, Luo P, et al. Semantic image segmentation via deep parsing network. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 1377--1385. Google Scholar

[18] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014,. arXiv Google Scholar

[19] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, 2012. 1097--1105. Google Scholar

[20] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015. Google Scholar

[21] Kan M, Shan S, Zhang H. Multi-View Discriminant Analysis.. IEEE Trans Pattern Anal Mach Intell, 2016, 38: 188-194 CrossRef PubMed Google Scholar

[22] Weiss Y, Torralba A, Fergus R. Spectral hashing. In: Proceedings of Advances in Neural Information Processing Systems, Vancouver, 2008. 1753--1760. Google Scholar

[23] Wu X, Hauptmann A G, Ngo C W. Practical elimination of near-duplicates from web video search. In: Proceedings of the 15th ACM International Conference on Multimedia, New York, 2007. 218--227. Google Scholar

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