# Emotions and Colors in a Design Archiving System: Applying AI Technology for Museums
## 4 Results  
### Matching Clustered Color and IRI Color to Extract Main Colors to Validate the Results  
In this research, it was important to rely on IRI colors to extract emotions from design works at the end, because IRI colors are closely related to emotional adjectives. We extracted three clustered colors using \(K\)-means clustering, and it was crucial to closely match the colors with IRI monochrome colors. Therefore, in this section, we will compare the clustered colors and IRI colors to extract the final three main colors, which were calculated using Euclidean distance analysis. The color values were compared with RGB values, and we analyzed the correlation to see how the colors matched each other.  
Images of 1214 pieces of design works were randomly collected from the Mu-um artist database and digital archiving website. Cultural journalists, exhibition organizers, collectors, and others can obtain more information about artists from this website. There are artworks by famous artists, but they especially collect images from Korean artists. Artists register their biography and portfolio and upload their design works as images on this website. The main colors were extracted from these digitized images. Figure 7 shows a comparison of the RGB values of the dominant colors of the design work measured by clustering and IRI colors matched by Euclidean distance. We extracted three dominant colors from the design works, and the color values were compared by calculating the correlation. The correlation coefficient between the RGB values of the dominant colors measured by clustering and IRI colors matched by Euclidean distance was more than 0.9731 for the first dominant color of the design works. The correlation coefficients for the second and third dominant colors were more than 0.9685 and 0.9715, respectively. This positive correlation coefficient indicated that the distance between the RGB values of the dominant colors measured by clustering and IRI colors was the closest, and these were well matched when applying Euclidean distance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Dc** & **IRIcs** &
\begin{tabular}{c} **Proportion** \\ **(\%)** \\ \end{tabular} & **D** & **Ea** & **Da** \\ \hline
1 & 211, 139, 40 & 219, 145, 72 & 46.0222 & 33.5261 & Springy & Decorative \\
2 & 200, 164, 146 & 199, 147, 168 & 30.7389 & 27.8208 & Clear & Light \\
3 & 161, 67, 24 & 151, 99, 56 & 23.2389 & 46.3465 & Decent & Modest \\ \hline \hline \end{tabular}  
* * Dc: Dominant color’s RGB value; IRIc: IRI 120 colors’ RGB value; D: Euclidean distance between DC and IRIc; Ea: emotional adjective from IRI color scale; Da: descriptive adjective from IRI color scale.  
\end{table}
Table 2: [PERSON]’s dominant colors and adjectives for emotions *.  
Table 3 shows the IRI RGB color numbers of 12 emotional adjectives from 1214 design works that were measured for their dominant colors. This shows how the museum archiving system classified the IRI RGB color according to emotional adjectives, and the data became a part of metadata of the system. We eliminated duplicated color numbers in the table. For the adjective springy, it had 128 design works. We identified that the colors in springy were located in the second quadrant in the IRI color and adjective scale (see Figure 6B). For the adjective high, it had 354 design works. We identified that the colors in high were located in the fourth quadrant in the IRI color and adjective scale. Other emotional adjectives were also analyzed for their content of color, and we were able to categorize the colors according to these emotional adjectives. The emotional adjectives and IRI colors were added to metadata, so it became possible to build the museum archiving system and retrieve design works based on colors.  
Figure 7: Comparison of the color values or RGB between the clustered dominant color of the design works and then matched with IRI color.