# Emotions and Colors in a Design Archiving System: Applying AI Technology for Museums
## 6 Conclusions  
This study proposed a new archiving system for museums using emotions. A system was built in this study that used the main colors and emotional adjectives of design works by utilizing the IRI image scale to extract emotions. AI was used to construct metadata by extracting the main colors. A \(K\)-means clustering technique to generate clusters of image pixel values was used to derive the main colors in the design works. As three main colors had to be selected, the number of clusters was set to three to ensure efficiency. In this study, the Euclidean distance method was used to match the IRI's monochromatic colors and adjectives that described the emotions of the design works. To assess the reliability of the relation of the extracted main colors and IRI colors, we calculated the coefficient of RGB values, and the result was impressive, with a correlation of more than 0.97 on average. The main colors of the design works and their characteristics were finally selected and stored in an archiving database. An image retrieval system was constructed by adding new metadata. By adding the emotion data of the design works to the database, we attempted to diversify the archiving data units and to transform intangible aesthetic semantics into data. To see the possibility of application and utilization of the system, we organized a virtual exhibition with images of Korean design history from the system to show the design works with emotions. The result was an attempt to enable a search for design works based on their content and emotions without depending on the title or name of an artist, enabling curators and museum managers to organize new concepts for exhibitions. In the future, the functionality of the database and the efficiency of searching design works with this system are expected to be improved by adding other data units that represent the various characteristics of design works.  
Libraries and museums in the field of culture and arts management also provide the public with online materials and services. Books and works of art, which previously only existed in tangible form, are now electronically provided and managed online. In addition to the digitization of analog materials, AI is being actively used to automate and enhance the efficiency of using large quantities of data.  
Museums and galleries are also changing due to digital technology. In the process of collecting design works, they are constructing institutional archives and working on documenting collections in digital data formats [4; 5; 6]. Collection and preservation work in museums is an area in which collection managers number, catalog, collect, and preserve design works with artistic value [4]. These most fundamental of museum activities help in the curation of exhibitions; therefore, digital database construction can utilize relevant data [7]. In conclusion, the current study is expected to provide some information on the right direction for new color utilization with adjectives for recognition of feelings.  
Conceptualization, H.J.K. and H.-K.L.; methodology, H.J.K. and H.-K.L.; software, H.J.K.; validation, H.J.K. and H.-K.L.; formal analysis, H.J.K.; investigation, H.J.K. and H.-K.L.; resources, H.J.K. and H.-K.L.; data curation, H.J.K.; writing--original draft preparation,H.J.K. and H.-K.L.; writing--review and editing, H.J.K. and H.-K.L.; visualization, H.J.K. and H.-K.L.; supervision, H.-K.L.; project administration, H.J.K.; funding acquisition, H.-K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research Foundation of Korea: 2015R1A5A7037825 and Yonsei University Research Grant of 2020.  
Not applicable.  
The data from the result in this study are available from the corresponding author on request.  
The authors declare no conflict of interest.