[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
research-article

ColorNetVis: An Interactive Color Network Analysis System for Exploring the Color Composition of Traditional Chinese Painting

Published: 15 April 2024 Publication History

Abstract

In the field of digital humanities, color research aims to discover explanations for painting history and color usage habits. However, researchers analyzing color relationships is challenging and time-consuming, as it requires color extraction and a detailed review of many painting images for reference and comparison of color relationships. In our work, we propose ColorNetVis, an interactive color network analysis tool that enables researchers to explore color relationships through color networks. The core of ColorNetVis is a bipartite network model that establishes a bipartite relationship between colors and Chinese painting within a scope based on color difference measurement. It constructs a one-mode color network through projection algorithms and similarity calculation methods to discover the relationship between colors. We propose a coordinated set of views to demonstrate the combination of determined color networks with painting types and real-world attributes. We use color space view, color attribute distribution view, and single color query components to assist researchers in conducting detailed color analysis and validation. Through case studies, researcher reviews, and user studies, we demonstrate that ColorNetVis can effectively help researchers discover knowledge of color relationships and potential color research directions.

References

[1]
P. Bonacich, “Some unique properties of eigenvector centrality,” Social Netw., vol. 29, no. 4, pp. 555–564, 2007.
[2]
C. Chan, E. Akleman, and J. Chen, “Two methods for creating chinese painting,” in Proc. 10th Pacific Conf. Comput. Graph. Appl., 2002, pp. 403–412.
[3]
Y. Chang, H. Ma, L. Chang, and Z. Li, “Community detection with attributed random walk via seed replacement,” Front. Comput. Sci., vol. 16, no. 5, 2022, Art. no.
[4]
P. Chunaev, “Community detection in node-attributed social networks: A survey,” Comput. Sci. Rev., vol. 37, 2020, Art. no.
[5]
Z. Feng, W. Yuan, C. Fu, J. Lei, and M. Song, “Finding intrinsic color themes in images with human visual perception,” Neurocomputing, vol. 273, pp. 395–402, 2018.
[6]
B. Flueckiger, “A digital humanities approach to film colors,” Moving Image: J. Assoc. Moving Image Archivists, vol. 17, no. 2, pp. 71–94, 2017.
[7]
B. Flueckiger and G. Halter, “Methods and advanced tools for the analysis of film colors in digital humanities,” DHQ: Digit. Humanities Quart., vol. 14, no. 4, 2020.
[8]
T. M. Fruchterman and E. M. Reingold, “Graph drawing by force-directed placement,” Softw.: Pract. Experience, vol. 21, no. 11, pp. 1129–1164, 1991.
[9]
L. T. Goldsmith, “Wang yani: Stylistic development of a chinese painting prodigy,” Creativity Res. J., vol. 5, no. 3, pp. 281–293, 1992.
[10]
J.-L. Guillaume and M. Latapy, “Bipartite graphs as models of complex networks,” Physica A: Stat. Mechanics Appl., vol. 371, no. 2, pp. 795–813, 2006.
[11]
S. Haghani and M. R. Keyvanpour, “A systemic analysis of link prediction in social network,” Artif. Intell. Rev., vol. 52, pp. 1961–1995, 2019.
[12]
X. Haoran, C. Shuyao, and Y. Zhang, “Magical brush: A symbol-based modern chinese painting system for novices,” in Proc. Proc. CHI Conf. Hum. Factors Comput. Syst., 2023, pp. 1–14.
[13]
D. Hu, “The philosophical origin of the black-white system of chinese painting,” Int. J. Politics, Culture, and Society, 1995, pp. 453–465.
[14]
S. Jiang, Q. Huang, Q. Ye, and W. Gao, “An effective method to detect and categorize digitized traditional chinese paintings,” Pattern Recognit. Lett., vol. 27, no. 7, pp. 734–746, 2006.
[15]
Z. Jin, Y. Zhang, J. Miao, Y. Yang, Y. Zhuang, and Y. Pan, “A knowledge-guided and traditional chinese medicine informed approach for herb recommendation,” Front. Inf. Technol. Electron. Eng., vol. 24, no. 10, pp. 1416–1429, 2023.
[16]
C. Jingwena, L. Ruibinga, X. Pinghuaa, J. Jinga, and X. Minghuia, “Automatic imagery coloration and regenerative design of ethnic costume,” in Design Studies and Intelligence Engineering, vol. 365. Amsterdam: IOS Press, 2023.
[17]
G. M. Johnson, X. Song, E. D. Montag, and M. D. Fairchild, “Derivation of a color space for image color difference measurement,” Color Res. Appl., vol. 35, no. 6, pp. 387–400, 2010.
[18]
A. Kaneko, A. Komatsu, T. Itoh, and F. Y. Wang, “Painting image browser applying an associate-rule-aware multidimensional data visualization technique. Visual Computing for Industry,” Biomedicine, Art, vol. 3, no. 1, pp. 1–13, 2020.
[19]
A. Kumar, S. S. Singh, K. Singh, and B. Biswas, “Link prediction techniques, applications, and performance: A survey,” Physica A: Stat. Mechanics Appl., vol. 553, 2020, Art. no.
[20]
M. Li, Y. Wang, and Y.-Q. Xu, “Computing for chinese cultural heritage,” Vis. Informat., vol. 6, no. 1, pp. 1–13, 2022.
[21]
R. Li, X. Jia, C. Zhou, and J. Zhang, “Reconfiguration of the brain during aesthetic experience on chinese calligraphy–using brain complex networks,” Vis. Informat., vol. 6, no. 1, pp. 35–46, 2022.
[22]
Y. Li, X. Liu, Y. Sun, and C. Lu, “Color adjacent network model for product color design,” Comput. Integr. Manuf. Syst., vol. 25, no. 9, pp. 2255–2364, 2019.
[23]
K. Liang et al., “Analysis of the application skills of color in the creation of characters in traditional chinese painting,” Front. Art Res., vol. 3, no. 1, 2021.
[24]
X. Liu, Y. Cao, and L. Zhao, “Color networks of traditional cultural patterns and color design aiding technology,” Comput. Integr. Manuf. Syst., vol. 22, no. 4, pp. 899–907, 2016.
[25]
X. Liu, Y. Zhu, and X. Wu, “Joint user profiling with hierarchical attention networks,” Front. Comput. Sci., vol. 17, no. 3, 2023, Art. no.
[26]
S. Lonapalawong et al., “Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition,” Front. Inf. Technol. Electron. Eng., vol. 23, no. 3, pp. 382–397, 2022.
[27]
M. R. Luo, G. Cui, and B. Rigg, “The development of the cie 2000 colour-difference formula: Ciede2000,” Color Res. Appl.: Endorsed by Inter-Soc. Color Council, Colour Group (Great Britain), Can. Soc. Color, Color Sci. Assoc. Jpn., Dutch Soc. Study Color, Swedish Colour Centre Found., Colour Soc. Aust., Centre Français de la Couleur, vol. 26, no. 5, pp. 340–350, 2001.
[28]
X. Mao, J. Xu, J. Lang, and S. Zhang, “Visualization of isomorphism-synesthesia of colour and music,” Vis. Inform., vol. 7, no. 4, pp. 110–114, 2023.
[29]
S. Pei and Y. Chiu, “Background adjustment and saturation enhancement in ancient chinese paintings,” IEEE Trans. Image Process., vol. 15, no. 10, pp. 3230–3234, Oct. 2006.
[30]
S.-C. Pei, Y.-C. Zeng, and C.-H. Chang, “Virtual restoration of ancient chinese paintings using color contrast enhancement and lacuna texture synthesis,” IEEE Trans. Image Process., vol. 13, no. 3, pp. 416–429, Mar. 2004.
[31]
C. Sari, A. A. Salah, and A. A. A. Salah, “Automatic detection and visualization of garment color in western portrait paintings,” Digit. Scholarship Humanities, vol. 34, no. (Supplement_1), pp. i156–i171, 2019.
[32]
J. Silbergeld, “Chinese concepts of old age and their role in chinese painting, painting theory, and criticism,” Art J., vol. 46, no. 2, pp. 103–114, 1987.
[33]
E. Stepanova, “The impact of color palettes on the prices of paintings,” Empirical Econ., vol. 56, pp. 755–773, 2019.
[34]
D. A. Szafir, “Modeling color difference for visualization design,” IEEE Trans. Vis. Comput. Graphics, vol. 24, no. 1, pp. 392–401, Jan. 2018.
[35]
A. Thudt, U. Hinrichs, and S. Carpendale, “The bohemian bookshelf: Supporting serendipitous book discoveries through information visualization,” in Proc. Proc. SIGCHI Conf. Hum. Factors Comput. Syst., 2012, pp. 1461–1470.
[36]
D. J. Vartija, The Color of Equality: Race and Common Humanity in Enlightenment Thought. Philadelphia, USA: University of Pennsylvania Press, 2021.
[37]
M. Wu, Y. Sun, and Y. Li, “Adaptive transfer of color from images to maps and visualizations,” Cartogr. Geographic Inf. Sci., vol. 49, no. 4, pp. 289–312, 2022.
[38]
B. Xu, X. Liu, C. Lu, T. Hong, and Y. Zhu, “Transferring the color imagery from an image: A color network model for assisting color combination,” Color Res. Appl., vol. 44, no. 2, pp. 205–220, 2019.
[39]
M. Xu, P. Xu, Q. Wei, X. Ding, and H. Mao, “Color parsing of female brand clothing based on nexus network modeling,” J. Text Res., vol. 42, pp. 137–142, 2021.
[40]
G. Yang, S. Gao, Z. Feng, L. Wang, and Y. Xu, “Semantics expression of peking opera painted faces based on color metrics,” in Proc. Diversity, Divergence, Dialogue: 16th Int. Conf., Beijing, China, 2021, pp. 490–501, 2021.
[41]
R. Yu and B. Tan, “Construction of color network model of folk painting based on machine learning,” in Proc. Int. Conf. Mach. Learn. Cyber Secur., 2022, pp. 251–264.
[42]
S. A. Yun and Y.-I. Kim, “Fashion image digital color analysis method,” Color Res. Appl., vol. 44, no. 1, pp. 115–124, 2019.
[43]
C. Zhang et al., “Promotionlens: Inspecting promotion strategies of online e-commerce via visual analytics,” IEEE Trans. Vis. Comput. Graphics, vol. 29, no. 1, pp. 767–777, Jan. 2023.
[44]
J. Zhang and Y. Luo, “Degree centrality, betweenness centrality, and closeness centrality in social network,” in Proc. 2nd Int. Conf. Modell. Simul. Appl. Math., 2017, pp. 300–303.
[45]
M. Zhang, T. He, and M. Dong, “Meta-path reasoning of knowledge graph for commonsense question answering,” Front. Comput. Sci., vol. 18, no. 1, 2024, Art. no.
[46]
W. Zhang et al., “CohortVA: A visual analytic system for interactive exploration of cohorts based on historical data,” IEEE Trans. Vis. Comput. Graphics, vol. 29, no. 1, pp. 756–766, Jan. 2023.
[47]
Zhejiang University A, “Comprehensive collection of ancient chinese paintings in national museum exhibition,” Sep. 2022.
[48]
T. Zhou, J. Ren, M. Medo, and Y.-C. Zhang, “Bipartite network projection and personal recommendation,” Phys. Rev. E, vol. 76, no. 4, 2007, Art. no.
[49]
B. Zhu and J. Zhu, “Application of intelligent image color technology in teaching chinese painting color,” Secur. Commun. Netw., vol. 2022, pp. 1–12, 2022.
[50]
K. A. Zweig and M. Kaufmann, “A systematic approach to the one-mode projection of bipartite graphs,” Social Netw. Anal. Mining, vol. 1, pp. 187–218, 2011.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics  Volume 30, Issue 6
June 2024
202 pages

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 15 April 2024

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 26 Dec 2024

Other Metrics

Citations

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media