Analysis of the Application of Artificial Intelligence Technology in the Digital Processing of Traditional Visual Art Elements

[1]Gultepe, E., Conturo, T. E., & Makrehchi, M. (2018). Predicting and grouping digitized paintings by style using unsupervised feature learning. Journal of cultural heritage, 31, 13-23. GultepeE., ConturoT. E. & MakrehchiM. (2018). Predicting and grouping digitized paintings by style using unsupervised feature learning. Journal of cultural heritage, 31, 13-23.Search in Google Scholar

[2]Adamson, G. (2019). Thinking through craft. Bloomsbury Publishing. AdamsonG. (2019). Thinking through craft. Bloomsbury Publishing.Search in Google Scholar

[3]Jenicek, T., & Chum, O. (2019, September). Linking art through human poses. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 1338-1345). IEEE. JenicekT. & ChumO. (2019, September). Linking art through human poses. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 1338-1345). IEEE.Search in Google Scholar

[4]Strezoski, G., & Worring, M. (2018). Omniart: a large-scale artistic benchmark. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 14(4), 1-21. StrezoskiG. & WorringM. (2018). Omniart: a large-scale artistic benchmark. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 14(4), 1-21.Search in Google Scholar

[5]Fracchia, C. (2019). ‘Black But Human’: Slavery and Visual Art in Hapsburg Spain, 1480-1700. Oxford University Press. FracchiaC. (2019). ‘Black But Human’: Slavery and Visual Art in Hapsburg Spain, 1480-1700. Oxford University Press.Search in Google Scholar

[6]Bakhtiyorovich, K. I., & Shodikulovich, T. A. (2021). The Role of Painting in the Folk Art of Surkhandarya. International Journal of Human Computing Studies, 3(2), 26-28. BakhtiyorovichK. I. & ShodikulovichT. A. (2021). The Role of Painting in the Folk Art of Surkhandarya. International Journal of Human Computing Studies, 3(2), 26-28.Search in Google Scholar

[7]Lang, S., & Ommer, B. (2018). Attesting similarity: Supporting the organization and study of art image collections with computer vision. Digital Scholarship in the Humanities, 33(4), 845-856. LangS. & OmmerB. (2018). Attesting similarity: Supporting the organization and study of art image collections with computer vision. Digital Scholarship in the Humanities, 33(4), 845-856.Search in Google Scholar

[8]Rosenberg, F. J. (2017). After Human Rights: Literature, Visual Arts, and Film in Latin America, 1990-2010 (Vol. 74). University of Pittsburgh Press. RosenbergF. J. (2017). After Human Rights: Literature, Visual Arts, and Film in Latin America, 1990-2010 (Vol. 74). University of Pittsburgh Press.Search in Google Scholar

[9]Castellano, G., & Vessio, G. (2020). Towards a tool for visual link retrieval and knowledge discovery in painting datasets. In Digital Libraries: The Era of Big Data and Data Science: 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings 16 (pp. 105-110). Springer International Publishing. CastellanoG. & VessioG. (2020). Towards a tool for visual link retrieval and knowledge discovery in painting datasets. In Digital Libraries: The Era of Big Data and Data Science: 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings 16 (pp. 105-110). Springer International Publishing.Search in Google Scholar

[10]Adamopoulos, E., Rinaudo, F., & Ardissono, L. (2020). A critical comparison of 3D digitization techniques for heritage objects. ISPRS International Journal of Geo-Information, 10(1), 10. AdamopoulosE., RinaudoF. & ArdissonoL. (2020). A critical comparison of 3D digitization techniques for heritage objects. ISPRS International Journal of Geo-Information, 10(1), 10.Search in Google Scholar

[11]Castellano, G., Lella, E., & Vessio, G. (2021). Visual link retrieval and knowledge discovery in painting datasets. Multimedia Tools and Applications, 80, 6599-6616. CastellanoG., LellaE. & VessioG. (2021). Visual link retrieval and knowledge discovery in painting datasets. Multimedia Tools and Applications, 80, 6599-6616.Search in Google Scholar

[12]Berman, K. (2018). Finding voice: A visual arts approach to engaging social change (p. 249). University of Michigan Press. BermanK. (2018). Finding voice: A visual arts approach to engaging social change (p. 249). University of Michigan Press.Search in Google Scholar

[13]Kizhner, I., Terras, M., Rumyantsev, M., Khokhlova, V., Demeshkova, E., Rudov, I., & Afanasieva, J. (2021). Digital cultural colonialism: measuring bias in aggregated digitized content held in Google Arts and Culture. Digital Scholarship in the Humanities, 36(3), 607-640. KizhnerI., TerrasM., RumyantsevM., KhokhlovaV., DemeshkovaE., RudovI. & AfanasievaJ. (2021). Digital cultural colonialism: measuring bias in aggregated digitized content held in Google Arts and Culture. Digital Scholarship in the Humanities, 36(3), 607-640.Search in Google Scholar

[14]Petho, Á. (2023). Tacita Dean’s Affective Intermediality: Precarious Visions in-between the Visual Arts, Cinema, and the Gallery Film. Arts 12: 168. PethoÁ. (2023). Tacita Dean’s Affective Intermediality: Precarious Visions in-between the Visual Arts, Cinema, and the Gallery Film. Arts 12: 168.Search in Google Scholar

[15]Heywood, I., & Sandywell, B. (Eds.). (2017). The handbook of visual culture. Bloomsbury Publishing. HeywoodI. & SandywellB. (Eds.). (2017). The handbook of visual culture. Bloomsbury Publishing.Search in Google Scholar

[16]Pavlou, V. (2024). Drawing from pedagogy to policy: reimagining new possibilities for online art learning for generalist elementary teachers. Arts Education Policy Review, 125(4), 393-405. PavlouV. (2024). Drawing from pedagogy to policy: reimagining new possibilities for online art learning for generalist elementary teachers. Arts Education Policy Review, 125(4), 393-405.Search in Google Scholar

[17]Castellano, G., Digeno, V., Sansaro, G., & Vessio, G. (2022). Leveraging knowledge graphs and deep learning for automatic art analysis. Knowledge-Based Systems, 248, 108859. CastellanoG., DigenoV., SansaroG. & VessioG. (2022). Leveraging knowledge graphs and deep learning for automatic art analysis. Knowledge-Based Systems, 248, 108859.Search in Google Scholar

[18]BATIČ, J., & KRAŠNA, P. (2019). Developing Multimodal Literacy in Visual Art Education Classes. Contemporary Themes in Early Childhood Education and International Educational Modules, 151. BATIČJ. & KRAŠNAP. (2019). Developing Multimodal Literacy in Visual Art Education Classes. Contemporary Themes in Early Childhood Education and International Educational Modules, 151.Search in Google Scholar

[19]Impett, L., & Offert, F. (2022). There is a digital art history. Visual Resources, 38(2), 186-209. ImpettL. & OffertF. (2022). There is a digital art history. Visual Resources, 38(2), 186-209.Search in Google Scholar

[20]Ulger, K. (2018). The effect of problem-based learning on the creative thinking and critical thinking disposition of students in visual arts education. Interdisciplinary Journal of Problem-Based Learning, 12(1). UlgerK. (2018). The effect of problem-based learning on the creative thinking and critical thinking disposition of students in visual arts education. Interdisciplinary Journal of Problem-Based Learning, 12(1).Search in Google Scholar

[21]Raimo, N., De Turi, I., Ricciardelli, A., & Vitolla, F. (2022). Digitalization in the cultural industry: evidence from Italian museums. International Journal of Entrepreneurial Behavior & Research, 28(8), 1962-1974. RaimoN., De TuriI., RicciardelliA. & VitollaF. (2022). Digitalization in the cultural industry: evidence from Italian museums. International Journal of Entrepreneurial Behavior & Research, 28(8), 1962-1974.Search in Google Scholar

[22]Pelowski, M., Markey, P. S., Forster, M., Gerger, G., & Leder, H. (2017). Move me, astonish me… delight my eyes and brain: The Vienna Integrated Model of top-down and bottom-up processes in Art Perception (VIMAP) and corresponding affective, evaluative, and neurophysiological correlates. Physics of life reviews, 21, 80-125. PelowskiM., MarkeyP. S., ForsterM., GergerG. & LederH. (2017). Move me, astonish me… delight my eyes and brain: The Vienna Integrated Model of top-down and bottom-up processes in Art Perception (VIMAP) and corresponding affective, evaluative, and neurophysiological correlates. Physics of life reviews, 21, 80-125.Search in Google Scholar

[23]Hanrahan, N. W. (2018). Hearing the contradictions: Aesthetic experience, music and digitization. Cultural Sociology, 12(3), 289-302. HanrahanN. W. (2018). Hearing the contradictions: Aesthetic experience, music and digitization. Cultural Sociology, 12(3), 289-302.Search in Google Scholar

[24]Alexander, V. D. (2020). Sociology of the arts: Exploring fine and popular forms. John Wiley & Sons. AlexanderV. D. (2020). Sociology of the arts: Exploring fine and popular forms. John Wiley & Sons.Search in Google Scholar

[25]Moens, B. G. (2018). Aesthetic experience in virtual museums: A postphenomenological perspective. Studies in Digital Heritage, 2(1), 68-79. MoensB. G. (2018). Aesthetic experience in virtual museums: A postphenomenological perspective. Studies in Digital Heritage, 2(1), 68-79.Search in Google Scholar

[26]Pelowski, M., Leder, H., & Tinio, P. P. (2017). Creativity in the visual arts. The Cambridge handbook of creativity across domains, 80-109. PelowskiM., LederH. & TinioP. P. (2017). Creativity in the visual arts. The Cambridge handbook of creativity across domains, 80-109.Search in Google Scholar

[27]Li, P. (2021). Research on visual art design method based on virtual reality. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 13(2), 16-25. LiP. (2021). Research on visual art design method based on virtual reality. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 13(2), 16-25.Search in Google Scholar

[28]Haitao Wang, Huakun Zhou, Yanyan Chen, Liu Yang & Wenfeng Bi. (2025). Deep learning GAN-based fault detection and diagnosis method for building air-conditioning systems. Sustainable Cities and Society 106068-106068. WangHaitao, ZhouHuakun, ChenYanyan, YangLiu & BiWenfeng. (2025). Deep learning GAN-based fault detection and diagnosis method for building air-conditioning systems. Sustainable Cities and Society 106068-106068.Search in Google Scholar

[29]Mohammad Arafah, Iain Phillips, Asma Adnane, Wael Hadi, Mohammad Alauthman & Abedal Kareem Al Banna. (2025). Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks. Applied Soft Computing 112455-112455. ArafahMohammad, PhillipsIain, AdnaneAsma, HadiWael, AlauthmanMohammad & Al BannaAbedal Kareem. (2025). Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks. Applied Soft Computing 112455-112455.Search in Google Scholar

[30]Song Yang, Fenghua Ling, Jing Jia Luo & Lei Bai. (2024). Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method. Advances in Atmospheric Sciences(1), 26-35. YangSong, LingFenghua, LuoJing Jia & BaiLei. (2024). Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method. Advances in Atmospheric Sciences(1), 26-35.Search in Google Scholar