## 4 Experiments and Results
### Evaluation Experiments Using Representations Extracted from a Fine-Tuned Image Encoder  
We evaluate the fine-tuned image encoder using different experimental settings in this section. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**hexCode** & **Name** & **hexCode** & **Name** \\ \hline
01 ba8e & Technology Turquoise & dc13 bb & Plurbin \\
04196b & Bottle Cap Blue Juice & df1c8a & Punk Dellah \\
0f1612 & Haha Its Not Black & e255f2 & Aortal \\
11 alife & Its Definitely Not Pink & e5149a & Elle Woods Was Here \\
16d54d & Gentiha & e5 caeb & Light Salvia \\
173838 & Fredero & e8c251 & Luigi Yellow \\
174855 & Business Blue & e98425 & True October \\
176b76 & Dweebith & eea4835 & Piper Pizza Red \\
20654d & Pompoono & eca741 & Yeach \\
2388 fb & Dragonbreaker147 & e4 dbc3 & Violent Barbie Doll \\
243a47 & Autumn Storm in the Mountains & f22966 & Vadelma \\
260b84 & Gibbet & f5221b & Old Liquorish \\
2d31c3 & Creeping Dusk & f57c7e & White Girl Sunburn Rash \\
341e50 & Earthy Pearthy & fc7420 & Dinner in the Desert \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Examples of color names collected from colormames.org. Figure 16: Linguistic space visualization. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pre-Trained** & **Query** & **Rank 1** & **Rank 2** & **Rank 3** & **Rank 4** & **Rank 5** \\ \hline BERT-base-uncased & & Olayan & Fredoro & Polar Purple & Luigi Yellow & Toxic Mermaid \\  & & & & & & \\  & Electric Violet & & & & & \\ \hline BERT-base-multilingual-uncased & & Red Purple & Luigi Yellow & Polar Purple & Vadelma & Light Salvia \\ \hline  & & & & & & \\ \cline{2-5} Fine-tuned CLIP (ours) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Example of color-based similar description retrieval. 4.4.1 Pearson Correlation Coefficient Calculation on the CN Dataset and the Color Card Image Dataset  
The CN dataset and corresponding color cards were used to evaluate cross-modal performance by calculating the Pearson correlation coefficient score between text descriptions and color card images. As results shown in Table 9, the cross-modal performance is increased after the fine-tuning process. 4.4.2 Similarity Score Calculation on the Same Ukiyo-e Print Digitized at the Different Institutions  
First, as mentioned in Section 1, some of the image similarity calculation application can be used in artwork retrieval systems. Therefore, we conducted an experiment to check the performance of them. We collected three different versions of the same ukiyo-e print, as shown in Figure 17, and compared two similarity calculation applications [15,16] with all image pairs as inputs. The results are shown in Table 10. Although the detailed calculation methods of these two commercial applications are not explained, it can be seen that there is a large difference between input image 1 and the other two input images under both calculation applications. This is probably caused by color differences, angle changes, or any blemishes in different image versions. From this example we can assume that, for some of the artworks, it is indispensable to extract better color information and image sketch for better representation. In order to investigate whether the model, after fine-tuning, has an improved performance in the calculation of the similarity of the same ukiyo-e print digitized at the different institutions, we tested our fine-tuning method using the examples in Section 1.1. In order to evaluate whether the model is improved in the calculation of the similarity of both structures and colors, we added a set of comparative experiments, as shown in Table 11. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Pair** & **IMGonline [15]** & **DeepAI [16]** \\ \hline Input image (1,2) & Similarity\({}_{1,2}:81.80\%\) & Distance\({}_{1,2}:6\) \\ Input image (2,3) & Similarity\({}_{2,3}:90.05\%\) & Distance\({}_{2,3}:2\) \\ Input image (1,3) & Similarity\({}_{1,3}:87.11\%\) & Distance\({}_{2,3}:6\) \\ \hline \hline \end{tabular}
\end{table}