Generating Personalized Abstract Art Paintings by using People's Life Energy Distribution

Öz

Data visualization, which is essentially the visual expression of data through mathematical calculations, is a highly effective method for transferring and understanding data. The right data visualization, selected or developed with data and people in mind, has an important role in improving the quality of conveying the relationships, meanings, information, structures and hidden properties of data to individuals from different fields of expertise and enables them to work together. Data visualization has been a subject that has evolved and progressed in different ways throughout history and has finally reached today's computer technology and conditions. The improved facilities of today's computer such as high computing power has brought data visualization methods to a new horizon. Also the changing definition of the data has expanded the boundaries of the data visualization. Today, data is obtained from many different sources such as; IoT, Embedded systems, Social data, Business data and Real-Life data. Our study is also inspired by the Real-Life. We developed a visualization system based on real-life botanical trees and using humans as data. Throughout history, computer science and other branches of science have used art as a subject in various studies. In our study, we bring science and art together again to develop a way of expressing people through art. Thus, we developed an artistic visualization system that uses the activities of people's lives as data and generates artistic visuals inspired by abstract art paintings. Through this work, we have created an art style with a visualization system that manages to produce artistic visuals while adhering to its mathematical foundations.

Anahtar Kelimeler

• Data Visualization
• Creative Algorithms
• Computational Creativity
• Neural Style Transferring
• Abstract Art

Kaynakça

1. 1. S. Kumari, “What is meant by abstract painting?,” The Statesman, https://www.thestatesman.com/features/meant-abstract-painting-1503052291.html (accessed May 17, 2023)
2. 2. Rudolph Arnheim, Visual Thinking, University of California Press, 1969, ISBN 0-520-01871-0
3. 3. D. Nimmer, “What is abstract art (and why should I care)?,” Artists Network, https://www.artistsnetwork.com/art-inspiration/what-is-abstract-art/ (accessed May 17, 2023)
4. 4. D. E. Knuth, “Computer programming as an art,” Commun. ACM, vol. 17, no. 12, pp. 667–673, Dec. 1974, doi: 10.1145/361604.361612.
5. 5. Q. Li, “Data visualization as creative art practice,” Visual Communication, vol. 17, no. 3, pp. 299–312, Aug. 2018, doi: 10.1177/1470357218768202.
6. 6. F. B. Viégas and M. Wattenberg, “Artistic Data Visualization: Beyond Visual Analytics,” in Online Communities and Social Computing, D. Schuler, Ed., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2007, pp. 182–191. doi: 10.1007/978-3-540-73257-0_21.
7. 7. S. Alvaro, “Art and Data: the aesthetic emergence of knowledge.” in Proceedings of the 5th Computer Art Congress, 2016, Paris, France, pp. 108-125.
8. 8. S. Colton, R. Mántaras, and O. Stock, “Computational Creativity: Coming of Age,” AI Magazine, vol. 30, pp. 11–14, Sep. 2009, doi: 10.1609/aimag.v30i3.2257.

9. 9. M. Islam and S. Jin, "An Overview of Data Visualization," 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2019, pp. 1-7, doi: 10.1109/ICISCT47635.2019.9012031.
10. 10. H. -J. Schulz, "Treevis.net: A Tree Visualization Reference," in IEEE Computer Graphics and Applications, vol. 31, no. 6, pp. 11-15, Nov.-Dec. 2011, doi: 10.1109/MCG.2011.103
11. 11. G. A. Pavlopoulos, T. G. Soldatos, A. Barbosa-Silva, and R. Schneider, “A reference guide for tree analysis and visualization,” BioData Mining, vol. 3, no. 1, p. 1, Feb. 2010, doi: 10.1186/1756-0381-3-1.
12. 12. L. K. Long, L. C. Hui, G. Y. Fook, and W. M. N. Wan Zainon, “A Study on the Effectiveness of Tree-Maps as Tree Visualization Techniques,” Procedia Computer Science, vol. 124, pp. 108–115, Jan. 2017, doi: 10.1016/j.procs.2017.12.136.
13. 13. A. Kobsa, "User Experiments with Tree Visualization Systems," IEEE Symposium on Information Visualization, Austin, TX, USA, 2004, pp. 9-16, doi: 10.1109/INFVIS.2004.70.
14. 14. K. Onak and A. Sidiropoulos, “Circular partitions with applications to visualization and embeddings,” in Proceedings of the twenty-fourth annual symposium on Computational geometry, in SCG ’08. New York, NY, USA: Association for Computing Machinery, Jun. 2008, pp. 28–37. doi: 10.1145/1377676.1377683.
15. 15. E. Kleiberg, H. van de Wetering and J. J. van Wijk, "Botanical visualization of huge hierarchies," IEEE Symposium on Information Visualization, 2001. INFOVIS 2001., San Diego, CA, USA, 2001, pp. 87-94, doi: 10.1109/INFVIS.2001.963285.
16. 16. C. O. Wilke, Fundamentals of Data Visualization a Primer on Making Informative and Compelling Figures. Beijing: O’Reilly, 2019.
17. 17. J. Spence, E. Schachter, A. Saleem, and B. Jia, “Increasing Data-Knowledge Through Artistic Representation,” in HCI International 2021 - Posters, C. Stephanidis, M. Antona, and S. Ntoa, Eds., in Communications in Computer and Information Science, vol. 1419. Cham: Springer International Publishing, 2021, pp. 609–624. doi: 10.1007/978-3-030-78635-9_78.
18. 18. L. Devroye and P. Kruszewski, “The botanical beauty of random binary trees,” in Graph Drawing, F. J. Brandenburg, Ed., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 1996, pp. 166–177. doi: 10.1007/BFb0021801.
19. 19. D. M. de Vienne, “Lifemap: Exploring the Entire Tree of Life,” PLOS Biology, vol. 14, no. 12, p. e2001624, Dec. 2016, doi: 10.1371/journal.pbio.2001624.
20. 20. A. Sallaberry, Y.-C. Fu, H.-C. Ho, and K.-L. Ma, “ContactTrees: A Technique for Studying Personal Network Data.” arXiv, Oct. 31, 2014. doi: 10.48550/arXiv.1411.0052.
21. 21. S. Laurentiz, “Conformed Thought and the Art of Algorithms,” in Proceedings of the 9th International Conference on Digital and Interactive Arts, in ARTECH 2019. New York, NY, USA: Association for Computing Machinery, Feb. 2020, pp. 1–8. doi: 10.1145/3359852.3359871.
22. 22. C.-C. Lin, C.-B. Hsu, J.-C. Lee, C.-H. Chen, T.-M. Tu, and H.-C. Huang, “A Variety of Choice Methods for Image-Based Artistic Rendering,” Applied Sciences, vol. 12, p. 6710, Jul. 2022, doi: 10.3390/app12136710.
23. 23. W. Duch, “Computational Creativity,” in The 2006 IEEE International Joint Conference on Neural Network Proceedings, Jul. 2006, pp. 435–442. doi: 10.1109/IJCNN.2006.246714.
24. 24. Q. Cai, M. Ma, C. Wang, and H. Li, “Image neural style transfer: A review,” Computers and Electrical Engineering, vol. 108, p. 108723, May 2023, doi: 10.1016/j.compeleceng.2023.108723.
25. 25. L. A. Gatys, A. S. Ecker, and M. Bethge, “A Neural Algorithm of Artistic Style.” arXiv, Sep. 02, 2015. doi: 10.48550/arXiv.1508.06576.
26. 26. Egan, B. (2018). neural-style-pt (Version 1.0.0) [Computer software]. https://github.com/ProGamerGov/neural-style-pt