# Emotions and Colors in a Design Archiving System: Applying AI Technology for Museums
## 3 Methodology  
The current study proposed a design archiving system that stored the contents that illustrated the colors and emotions in design works by forming metadata using AI technology. A new data category--emotional adjectives and descriptive adjectives--which are coherent with the main colors of the design work, was added. This new category reinforced the traditional data classification method of storing, classifying, and retrieving design work information consisting only of the artist's name, production year, and style. The methodology was built as follows. To extract the dominant colors and save emotional adjectives related to the colors to the database, AI and automation technology were used. To collect emotional feature data from design work, we used the monochrome image scale and emotional adjective scale from the IRI Design Color Research Institute. To utilize the emotional characteristics of design works as a new way of archiving data, we extracted the three main colors used in the design work, matched the related emotional adjectives, and archived them as characteristics of the work. Finally, we implemented a searching system to study the effect of the utilization of the new system. Each pixel of the image was considered as one data point in a three-dimensional RGB (red, green, blue) data format. The colors ranged from 0 to 255. A clustering technique with RGB was performed to derive the main colors of the images in the design work. Clustering is unsupervised learning, a machine learning technique that clusters data with similar characteristics. Suppose there is a data set \(X=\left\{x_{i}\in R^{d}\middle|i=1,\cdots,N\right\}\) and disjoint subsets partitioning the data set into clusters \(S_{i}\), \(i=1,\cdots,L\)[37]. The clustering equation is as follows:  
\[J=\sum_{i=1}^{N}\sum_{k=1}^{K}\omega_{ik}\|x_{i}-\mu_{k}\|^{2} \tag{1}\]  
where \(\omega_{ik}=1\) if \(x_{i}\) is included in the subset \(S_{k}\), and 0 otherwise. \(\mu_{k}\) is the centroid as cluster center and mean value of cluster \(S_{k}\). The most widely used clustering criterion is the sum of the squared Euclidean distances between each data point \(x_{i}\) and centroid \(\mu_{k}\) of the subset \(S_{k}\), which contains \(x_{i}\) such that a clustering criterion is optimized [37]. In the current study, RGB values were used to manage design works in digital systems, and \(K\)-means clustering was used to cluster the colors of pixels to derive the main colors. The equation takes \(K\) centroids, finds the location of the center point \(K\) where the sum of the distances between the data points is minimized, and clusters the data surrounding the center point. This center point is eventually located at the mean value of each cluster's data, which is the reason it is called \(K\)-means clustering. The algorithm is executed only when the number of clusters (\(K\)) is set in advance; hence, \(K\) becomes a hyperparameter. The methodology and flow chart of this study as follows in Figure 4.  
In the present study, Python 3.0 was used for the analysis. We used the free, open-source Python Imaging Library (PIL) to perform a variety of image processing and graphics functions to extract the colors of the image and resize the image to 200 \(\times\) 200 in the Python interpreter. Since we extracted three dominant colors from each design work, we set K = 3 to create three clusters and conducted the analysis. To extract the main colors from the image for a pilot experiment, we selected the work of [PERSON], famous for experimental and colorful networks, along with [PERSON] and [PERSON], who were professors at Bauhaus. We used an image of [PERSON]'s _Senecio_ from the Mu-um digital archiving website to conduct the _K_-means clustering analysis, and the three dominant sets of RGB values were found to be (211, 139, 40), (200, 164, 146), and (161, 67, 24) in order, with ratios of 46%, 30.7%, and 23.2%, respectively (see Figure 5).