Abstract
In this paper, we focus on putting Faster-RCNN into practice to solve the problem of diplomatic video analysis, as the part of Mediated Public Diplomacy. Diplomatic video uploaded by U.S. Embassy in China is our research target. Using Faster-RCNN, we get 56,781 object detection results from those diplomatic videos. Then we use statistical tools to test the abnormal distribution of the object category “person”, clustering the above results so as to analyze the hidden strategic purposes in these diplomatic videos. Then we give an abstract of these videos: they mainly focus on common people’s high-quality life in the U.S. Strategic purposes are: the U.S. takes advantage of the repeating occurrence of common people to make “people to people” diplomacy in order to win hearts and minds of audience. Attractive personal life is depicted in the video so as to build a strong, harmonious and happy U.S. national image. These procedures are elaborately designed, which is a latent agenda setting process, and a fruitful frame construction attempt. By this way, the U.S. successfully bridge the culture gap and accomplish its goal of global hegemony.
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References
Banitalebi-Dehkordi A, Pourazad MT, Nasiopoulos P (2018) A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D)[J]. Multimed Tools Appl 76(22):1–32
Chang X, Yu YL, Yang Y et al (2017) Semantic pooling for complex event analysis in untrimmed videos[J]. IEEE Trans Pattern Anal Mach Intell 39(8):1617–1632
Chen X, Gupta A (2017) An implementation of faster RCNN with study for region sampling[J]
Entman RM (2008) Theorizing mediated public diplomacy: the U.S. case[J]. International Journal of Press/Politics 13(2):87–102
Fang Y, Zhang C, Yang W et al (2018) Blind visual quality assessment for image super-resolution by convolutional neural network[J]. Multimed Tools Appl 10:1–18
Girshick R (2015) Fast R-CNN[C]// IEEE International Conference on Computer Vision. IEEE Computer Society, p 1440–1448
Hariharan B, Arbeláez P, Girshick R et al (2014) Simultaneous detection and segmentation[C]// European conference on computer vision. Springer, Cham, pp 297–312
Hua C, Xiao TJ (2011) Moving object detection algorithm with gauss modeling based on FPGA[J]. Computer Engineering & Design 32(9):3000–3003
Jia Y, Shelhamer E, Donahue J, et al (2014) Caffe: convolutional architecture for fast feature embedding[C]// Proceedings of the 22nd ACM International Conference on Multimedia. ACM, p 675–678
Kendrick A, Fullerton JA (2004) Advertising as public diplomacy: attitude change among international audiences[J]. J Advert Res 44(3):297–311
Le THN, Zheng Y, Zhu C, et al (2016) Multiple scale Faster-RCNN approach to driver’s cell-phone usage and hands on steering wheel detection[C]// Computer Vision and Pattern Recognition Workshops. IEEE, 46–53
Liu H, Liu H, Sun F, Fang B (2018) Kernel regularized nonlinear dictionary learning for sparse coding. IEEE T Syst Man CY-S. https://doi.org/10.1109/TSMC.2017.2736248
Luo M, Chang X, Nie L et al (2017) An adaptive semi-supervised feature analysis for video semantic recognition.[J]. IEEE Trans Cybern 48(2):648–660
Ma Z, Chang X, Xu Z et al (2018) Joint attributes and event analysis for multimedia event detection.[J]. IEEE Trans Neural Netw Learn Syst 29(7):2921–2930
Ren S, He K, Girshick R, et al (2015) Faster R-CNN: towards real-time object detection with region proposal networks[C]// International Conference on Neural Information Processing Systems. MIT Press, p 91–99
Ren Y, Zhu C, Xiao S (2018) Object detection based on fast/faster RCNN employing fully convolutional architectures[J]. Math Probl Eng 1:1–7
Shamsolmoali P, Jain DK, Zareapoor M, et al (2018) High-dimensional multimedia classification using deep CNN and extended residual units[J]. Multimed Tools Appl:1–16
Sheafer T, Gabay I (2009) Mediated public diplomacy: a strategic contest over international agenda building and frame building[J]. Polit Commun 26(4):447–467
Zeng Z, Li Z, Cheng D et al (2018) Two-stream multi-rate recurrent neural network for video-based pedestrian re-identification[J]. IEEE T Ind Inform 14(7):3179–3186
Zhihui L, Feiping N, Xiaojun C et al (2017) Beyond trace ratio: weighted harmonic mean of trace ratios for multiclass discriminant analysis[J]. IEEE Trans Knowl Data Eng 29(10):2100–2110
Zhu C, Li G (2018) A multilayer backpropagation saliency detection algorithm and its applications[J]. Multimed Tools Appl 11:1–17
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This work was jointly supported in part by National Social Science Foundation under Grant 16BXW054 and the National Natural Science Foundation of China under Grant U1613212.
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Zhao, H., Zhou, F. & Liu, H. Deep learning for diplomatic video analysis. Multimed Tools Appl 79, 4811–4830 (2020). https://doi.org/10.1007/s11042-018-6650-9
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DOI: https://doi.org/10.1007/s11042-018-6650-9