banner

The role of iconography in shaping Chinese national identity: Analyzing its representation in visual media and political propaganda

HuiXia Zhen, Bo Han

Abstract


The creation of cultural iconography that may reflect national culture and encourage individuals to identify with Chinese culture has always been a difficult issue. In this study, we present a symbolic creation framework for Chinese national cultural identity constructed from visual pictures using generative adversarial networks (GAN). To enhance the structure collapse phenomena of generative adversarial systems, form search regular procedure and generator cross-loss factors on the basis of GAN should be combined. To enhance the real-time efficiency of the model by lowering the parameters in the model, the conventional convolutional component of the generator in the system’s architecture is substituted with a significant recoverable convolution. The notions of iconography and character as they relate to symbols are discussed in this essay.  It also advises using iconography as a technique of symbolic imagery to give emergent symbols identity.  The design in this study may create significant performance ethnic cultural symbols while preserving superior temporal performance, according to the findings of rigorous testing on real datasets, which may have practical application value. The accuracy, precision, recall, and F1 of the system in this study are 91.54%, 89.02%, 90.96%, and 87.48%.


Keywords


generative adversarial networks; accuracy; iconography; precision

Full Text:

PDF

References


1. Hu B, Zelenko O, Pinxit V, et al. A Social Semiotic Approach and a Visual Analysis Approach for Chinese Traditional Visual Language: A Case of Tea Packaging Design. Theory and Practice in Language Studies. 2019, 9(2): 168. doi: 10.17507/tpls.0902.06

2. Sun Z. Utopia, nostalgia, and femininity: visually promoting the Chinese Dream. Visual Communication. 2017, 18(1): 107-133. doi: 10.1177/1470357217740394

3. Gabore SM. Western and Chinese media representation of Africa in COVID-19 news coverage. Asian Journal of Communication. 2020, 30(5): 299-316. doi: 10.1080/01292986.2020.1801781

4. Jones EJ, Marsland AL, Kraynak TE, et al. Subjective Social Status and Longitudinal Changes in Systemic Inflammation. Annals of Behavioral Medicine. 2023, 57(11): 951-964. doi: 10.1093/abm/kaad044

5. Geisler ME. National Symbols, Fractured Identities: Contesting the National Narrative. UPNE; 2005.

6. Rembold E, Carrier P. Space and identity: constructions of national identities in an age of globalisation. National Identities. 2011, 13(4): 361-377. doi: 10.1080/14608944.2011.629425

7. Heath AF, Tilley JR. British national identity and attitudes towards immigration. International Journal on Multicultural Societies.2005, 7(2): 119-132.

8. Zhao J. Interpreting the cultural symbols in Chinese documentaries: taking a bite of China and beautiful China as examples. Frontiers in Educational Research. 2020, 3(11).

9. Wang D, Martin BAS, Yao J. Do Discount Presentations Influence Gift Purchase Intentions and Attitudes of Chinese Outbound Tourists? Journal of Travel Research. 2020, 60(5): 1104-1122. doi: 10.1177/0047287520918008

10. Han HC (Sandrine). Moving From Cultural Appropriation to Cultural Appreciation. Art Education. 2019, 72(2): 8-13. doi: 10.1080/00043125.2019.1559575

11. Bugaev VI. Pedagogical discourse in the system of color perception of art by future designers in the process of training. Samara Journal of Science. 2020, 9(4): 278-281. doi: 10.17816/snv202094302

12. Panyok T, Dai Z, Ju D. Project activities in the professional training of future designers (based on China’s educational experience). Art, Design & Communication in Higher Education. 2022, 21(1): 23-42. doi: 10.1386/adch_00045_1

13. Yang ZL, Zhang SY, Hu YT, et al. VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder. IEEE Transactions on Information Forensics and Security. 2021, 16: 880-895. doi: 10.1109/tifs.2020.3023279

14. Cai Z, Xiong Z, Xu H, et al. Generative Adversarial Networks. ACM Computing Surveys. 2021, 54(6): 1-38. doi: 10.1145/3459992

15. Taniguchi T, Nagai T, Nakamura T, et al. Symbol emergence in robotics: a survey. Advanced Robotics. 2016, 30(11-12): 706-728. doi: 10.1080/01691864.2016.1164622

16. Wang X, Gong J, Hu M, et al. LAUN Improved StarGAN for Facial Emotion Recognition. IEEE Access. 2020, 8: 161509-161518. doi: 10.1109/access.2020.3021531

17. Matsuo Y. Special Features of Deep Learning and Symbol Emergence. New Generation Computing. 2020, 38(1): 5-6. doi: 10.1007/s00354-020-00088-x

18. Yun XL, Zhang YM, Yin F, et al. Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams. IEEE Transactions on Multimedia. 2022, 24: 2580-2594. doi: 10.1109/tmm.2021.3087000

19. Mitra R, Jain S, Bhatia V. Least Minimum Symbol Error Rate Based Post-Distortion for VLC Using Random Fourier Features. IEEE Communications Letters. 2020, 24(4): 830-834. doi: 10.1109/lcomm.2020.2968026

20. Cheng G, Han J, Zhou P, et al. Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection. IEEE Transactions on Image Processing. 2019, 28(1): 265-278. doi: 10.1109/tip.2018.2867198

21. Iqbal T, Ali H. Generative Adversarial Network for Medical Images (MI-GAN). Journal of Medical Systems. 2018, 42(11). doi: 10.1007/s10916-018-1072-9

22. Feng J, Yu H, Wang L, et al. Classification of Hyperspectral Images Based on Multiclass Spatial–Spectral Generative Adversarial Networks. IEEE Transactions on Geoscience and Remote Sensing. 2019, 57(8): 5329-5343. doi: 10.1109/tgrs.2019.2899057

23. Gao H, Yang Y, Li C, et al. Multiscale Residual Network with Mixed Depthwise Convolution for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021, 59(4): 3396-3408. doi: 10.1109/tgrs.2020.3008286




DOI: https://doi.org/10.32629/jai.v7i3.1516

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 HuiXia Zhen, Bo Han

License URL: https://creativecommons.org/licenses/by-nc/4.0/