Automated Image-Based User Interface Color Theme Generation
Source abstract
Abstract
Color plays an essential role in the design of user interfaces and significantly impacts the user experience. To create aesthetically pleasing and user-friendly interfaces, the colors of the user interface should be consistent with the images. The latter can be challenging to achieve, as images often have different colors and are often changed by editors or authors who do not have sufficient design knowledge. To solve this problem, we have developed a model that automatically determines the color theme of the user interface based on a given image. The model first extracts the most prominent colors from the image and then considers both aesthetic (color harmony and compatibility with the image) and usability aspects (color contrast, color diversity, and color strength). All color calculations are performed in the perceptually uniform color space CAM02-UCS. In addition, the model can be adapted to the user’s needs and requirements. To test the model, we implemented it in a web-based application in which the colors were automatically selected based on the featured image. The resulting color themes were then evaluated by the users, who were mainly professional designers. According to the results, the model generates color themes that are consistent with the image, aesthetic, and user-friendly. An important observation was also that color harmony can be achieved simply by using the most prominent colors of the image (regardless of their hue), suggesting that color harmony is strongly influenced by the context of use. The presented model holds significant practical importance as it can be utilized in various applications and tools. For instance, it can automatically choose a color theme for a user interface based on a particular image, such as a company logo or a product image. Moreover, it can dynamically adjust the colors of elements in real time based on the image that is visible simultaneously with the elements.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Color perception and design · Color Science and Applications · Multisensory perception and integration
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Natural Sciences and Engineering, Department of Textiles, Graphic Arts and Design, University of Ljubljana, 1000 Ljubljana, Slovenia
Funding
Slovenian Research Agency
References (51)
Show source-provided references
1. Colorgorical: Creating discriminable and preferable color palettes for information visualization10.1109/TVCG.2016.2598918 ↗
2. Determining key colors from a design perspective using dE-means color clustering10.1002/col.22817 ↗
3. Color compatibility from large datasets
4. Aesthetic Rating and Color Suggestion for Color Palettes10.1111/cgf.13010 ↗
5. InfoColorizer: Interactive Recommendation of Color Palettes for Infographics10.1109/TVCG.2021.3085327 ↗
6. Data driven webpage color design10.1016/j.cad.2016.03.001 ↗
7. Influences of Color Salience and Location of Website Links on User Performance and Affective Experience with a Mobile Web Directory10.1080/10447318.2020.1838188 ↗
8. How do app icon color and border shape influence visual search efficiency and user experience? Evidence from an eye-tracking study10.1016/j.ergon.2021.103160 ↗
9. Tian, S.J., Luo, X., Lu, D., and Chen, Y. (2017, January 21–22). Study on the Effect of Web Color Scheme on User Behavior. Proceedings of the 2017 International Conference on Virtual Reality and Visualization (ICVRV 2017), Zhengzhou, China.10.1109/ICVRV.2017.00099 ↗
10. Effects of button colour and background on augmented reality interfaces10.1080/0144929X.2023.2183060 ↗
11. Effects of App Icon Border Form and Interface Background Color Saturation on User Visual Experience and Search Performance10.1155/2022/1166656 ↗
12. Lidwell, W., Holden, K., Butler, J., and Elam, K. (2010). Universal Principles of Design, Revised and Updated: 125 Ways to Enhance Usability, Influence Perception, Increase Appeal, Make Better Design Decisions, and Teach through Design, Rockport Publishers.
13. A study of colour emotion and colour preference. Part I: Colour emotions for single colours10.1002/col.20010 ↗
14. Investigation of human’s emotional responses on colors10.1002/col.20246 ↗
15. Colour appeal in website design within and across cultures: A multi-method evaluation10.1016/j.ijhcs.2009.08.005 ↗
16. Kopacz, J. (2012). Colour Design, Woodhead Publishing.
17. An ecological valence theory of human color preference10.1073/pnas.0906172107 ↗
18. Wright, A. (2012). Colour Design, Woodhead Publishing.
19. A study of colour emotion and colour preference. Part III: Colour preference Modeling10.1002/col.20047 ↗
20. A study of colour emotion and colour preference. part II: Colour emotions for two-colour combinations10.1002/col.20024 ↗
21. Colour Harmony
22. The role of spectral energy of source and background color in the pleasantness of object colors10.1364/AO.9.001513 ↗
23. Aesthetic response to color combinations: Preference, harmony, and similarity10.3758/s13414-010-0027-0 ↗
24. A colour harmony model for two-colour combinations10.1002/col.20208 ↗
25. Influence of a holistic color interval on color harmony10.1002/1520-6378(200102)26:1<29::AID-COL4>3.0.CO;2-B ↗
26. Experimental Determination of the Laws of Color Harmony. Part 4: Color Preference and the Color Harmony Content10.1002/col.20489 ↗
27. Feisner, E.A. (2006). Colour: How to Use Colour in Art and Design, Laurence King.
28. Judd, D.B., and Wyszecki, G. (1975). Color in Business, Science and Industry, Wiley. [3rd ed.].
29. Color harmony10.1002/col.10004 ↗
30. Neumann, L., Nemcsics, A., and Neumann, A. (2005, January 18–20). Computational color harmony based on coloroid system. Proceedings of the Proceedings of the First Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging, Girona, Spain.
31. Experimental Modeling of Colour Harmony10.1002/col.20558 ↗
32. A computer-assisted colour selection system based on aesthetic measure for colour harmony and fuzzy logic theory10.1002/col.20417 ↗
33. Morse, B.S., Thornton, D., Xia, Q., and Uibel, J. (October, January 16). Image-Based Color Schemes. Proceedings of the 2007 IEEE International Conference on Image Processing, San Antonio, TX, USA. Volume III.10.1109/ICIP.2007.4379355 ↗
34. Automatic color palette10.3934/ipi.2007.1.265 ↗
35. Image segmentation based on adaptive K-means algorithm10.1186/s13640-018-0309-3 ↗
36. Rejeb, I.B., Ouni, S., and Zagrouba, E. (November, January 30). Image Retrieval Using Spatial Dominant Color Descriptor. Proceedings of the 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Hammamet, Tunisia.10.1109/AICCSA.2017.127 ↗
37. Huang, S.-C. (2021). An Efficient Palette Generation Method for Color Image Quantization. Appl. Sci., 11.10.3390/app11031043 ↗
38. Li, A., and Bao, X. (2010, January 23–24). Extracting Image Dominant Color Features Based on Region Growing. Proceedings of the 2010 International Conference on Web Information Systems and Mining, Sanya, China.10.1109/WISM.2010.116 ↗
39. Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization10.1155/2009/451638 ↗
40. Development of a machine learning model for extracting image prominent colors10.1002/col.22485 ↗
41. Data-driven image color theme enhancement
42. Hierarchical emotional color theme extraction10.1002/col.21988 ↗
43. Improving user experience of color palette extraction by using interactive visualization based on hierarchical color model10.1016/j.ijhcs.2022.102924 ↗
44. Attention-aware color theme extraction for fabric images10.1177/0040517516685278 ↗
45. Finding intrinsic color themes in images with human visual perception10.1016/j.neucom.2017.07.043 ↗
46. Lin, S., and Hanrahan, P. (May, January 27). Modeling how people extract color themes from images. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France.
47. Uniform colour spaces based on CIECAM02 colour appearance model10.1002/col.20227 ↗
48. Moroney, N. (, January March). Usage Guidelines for CIECAM97s. Proceedings of the Society for Imaging Science and Technology: Image Processing, Image Quality, Image Capture, Systems Conference, Portland, OR, USA. Available online: https://www.researchgate.net/publication/220865484_Usage_guidelines_for_CIECAM97s.
49. Group, A.G.W. (2024, January 08). Web Content Accessibility Guidelines (WCAG) 2.1. Available online: https://www.w3.org/TR/WCAG21/.
50. Weingerl, P. (2024, January 08). Github—Automatic Color Theme Generation for User Interfaces Based on Featured Image. Available online: https://github.com/primozw/records-app-dev.
51. Colour meaning and context10.1002/col.22095 ↗