## 5 Discussion  
In the evaluation experiments, we evaluated the accuracy of ResNet, the comparison methods, and the proposed method based on the accuracy of the validation and test images. In this section, we discuss the features extracted by the proposed method, and compare the performances of ResNet and the proposed method in terms of the modern, rustic, and traditional styles. First, we discuss the features extracted by the proposed method. Figure 6 shows the ten most important features in the proposed method, and Figure 7 shows the proportion of features based on its generation style. From Figure 6, the most essential features are local feature histograms, followed by the color vectors and color histograms, which are texture features. However, local feature histograms, color vectors, and color histograms are used equally in the output tree, with the ratio of local feature histogram at 0.388, color vector at 0.390, and color histogram at 0.222. Therefore, the results indicated that texture and color information plays a significant role in the space ambiance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Styles** & **All** & **Japanese** & **Modern** & **Rustic** & **Scandinavian** & **Traditional** \\ \hline _p_-values & **0.006** & **0.010** & 0.286 & 0.072 & **0.007** & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [PERSON]’s _t_-test results for _F-measure_. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Japanese** & **Modern** & **Rustic** & **Scandinavian** & **Traditional** \\ \hline ResNet & 0.567 & **0.686** & 0.747 & 0.549 & **0.861** \\ Conventional & 0.721 & 0.358 & 0.725 & 0.591 & 0.464 \\ HC & 0.774 & 0.523 & 0.751 & 0.713 & 0.633 \\ H+CV & 0.830 & 0.537 & 0.714 & 0.704 & 0.552 \\ H+H & 0.726 & 0.391 & 0.721 & 0.676 & 0.568 \\ H+CH & 0.846 & 0.592 & 0.755 & 0.707 & 0.569 \\ S+CH & 0.847 & 0.603 & 0.799 & 0.758 & 0.599 \\ Proposed & **0.909** & 0.623 & **0.789** & **0.784** & 0.553 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F-measures of the test images of the methods. Moreover, we analyzed the feature type proportion of generated rules, shown in Figure 7. The proposed method utilizes 2160 features, categorized into three types; local feature histogram, color vector, and color histogram. Rustic-style room images contain walls and furniture with a rugged surface and are a texture feature style. From Figure 7, we found that the rustic style employs a more local feature histogram, a texture feature. On the other hand, from Figure 7, the Scandinavian style employs the color vector, one of the color features, the most for training. Scandinavian-style room images contain a lot of furniture with characteristic colors. From these results, we verified that the proposed method automatically extracts appropriate features according to each room style. This result supports the effectiveness of the proposed method. Next, we discuss the performances of ResNet and the proposed method in terms of the modern, rustic, and traditional styles. Table 5 shows that of the ResNet method, and Table 6 shows the \(FPproportion\) and false negative (\(FN\)) ratio of the modern, rustic, and  
Figure 6: Most important features in the proposed method. Figure 7: Proportion of features in the proposed method, which can be categorized into three types: color histogram, color vector, and local feature histogram. (**a**) Rustic. (**b**) Scandinavian. traditional styles for the proposed method. The \(FPproportion\) and \(FNratio\) were calculated as follows:  
\[FPproportion=\frac{FP}{TP+FP} \tag{2}\]  
\[FNratio=\frac{FN}{TP+FN} \tag{3}\]  
In Figures 5 and 6, when the models estimated the style as rustic, the most misestimated style was traditional. Figure 8 shows examples of a misestimated image for a more detailed analysis. Figure 8 shows that ResNet and the proposed method have a high probability of misestimating traditional-style rooms with similar features. Therefore, no performance difference between was observed ResNet and the proposed method on the rustic style.