## 4 Evaluation Experiment
### Performance Evaluation  
Figure 5 shows the accuracies of the methods and validation images and Table 3 shows the _F-measure_ of the test images for ResNet, the comparison methods, and the proposed method. The _F-measure_ equation is expressed as follows:  
\[\textit{F-measure}=\frac{2(\textit{Precision}*\textit{Recall})}{\textit{ Precision}+\textit{Recall}} \tag{1}\]  
Figure 5 shows that the conventional method has the lowest accuracy and the proposed method has the highest accuracy. To validate each comparison method, we compared the accuracy of the conventional method, method-HC, method-H+H, method-H+CH, method-S+CH, and the proposed method. Method-H+CH had the highest accuracy among \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Abbreviations**} & \multicolumn{4}{c}{**Algorithm**} \\ \cline{3-5}  & & \(-\) & \(+\) & \\ \hline Conventional & - & Steps 5-10 & & \\ \hline \hline \multirow{4}{*}{Histogram + Color} & \multirow{4}{*}{HC} & \multirow{4}{*}{Steps 5-10} & \multirow{4}{*}{Step 2 \(\rightarrow\)} & Extract 64-dimensional SURF features \(S_{ij}=\{S_{ij}^{k}:k=1,\ldots,K\}\) \\  & & & & for \(T_{ij}\). Then, extract the color feature \(\textbf{C}_{ij}=\{C_{ij}^{k}:k=1,\ldots,K\}\) \\ \cline{3-5}  & & & & corresponding to the pixels of each SURF feature \(S_{ij}\) from the color information of each training image \(T_{ij}\). Finally, the SURF feature \(S_{ij}^{k}\) is concatenated with color feature \(C_{ij}^{k}\) to create a 65-dimensional local feature \(F_{ij}=S_{ij}\oplus C_{ij}\). \\ \hline Histogram + Color Vector & H+CV & Steps 6-10 & Step 10 \(\rightarrow\) Create image features \(\textbf{IF}_{ij}=w\textbf{1}\textbf{H}_{ij}\oplus w2\cdot\textbf{CV}_{ij}\) \\ \hline Histogram + Histogram & H+H & Steps 7-10 & Step 10 \(\rightarrow\) Create image features \(\textbf{IF}_{ij}=w\textbf{1}\textbf{H}_{ij}\oplus w3\cdot\textbf{CH}_{ij}\) \\ \hline Histogram + Color Histogram & H+CH & Steps 7-10 & Step 10 \(\rightarrow\) Create image features \(\textbf{IF}_{ij}=w\textbf{1}\textbf{H}_{ij}\oplus w2\cdot\textbf{CV}_{ij}\oplus w3 \cdot\textbf{CH}_{ij}\) \\ \hline \multirow{4}{*}{SPM + Color Histogram} & \multirow{4}{*}{S+CH} & \multirow{4}{*}{Step 7} & Based on the number of hierarchical levels \(M\), the total number of divisions \(N=\sum_{k}4^{m}\) of each training image \(T_{ij}\) is calculated, and the image is divided into \(256/2^{m}\times 256/2^{m}\) to create a subdi -video image \(\textbf{I}=\{I_{ij}^{m}:n=1,\ldots,N\}\). \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abbreviations of comparison methods and algorithmic differences between the comparison methods and the proposed method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**H+CV**} & \multirow{2}{*}{**H+H**} & \multirow{2}{*}{**H+CH**} & \multirow{2}{*}{**S+CH**} & \multirow{2}{*}{**Proposed**} \\ \cline{1-1} \cline{6-6}  & & & & & \\ \hline The optimal number & 45 & 50 & 25 & 50 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The optimal number of clusters for each method in color features. Figure 5: Validation accuracies of the methods. method-HC, method-H+CV, method-H+H, and method-H+CH. The accuracy was highest in terms of the average accuracy in round 0-30 in the following order: the conventional method, method-H+CH, method-S+CH, and the proposed method. Thus, histograms corresponding to the representative colors of the image were effective as a color feature, and the effectiveness of each algorithm in the proposed method was demonstrated. Conventionally, the space ambiance was extracted by BoVW, but the results confirm that the methods using histogram, SPM, and OD are effective for space ambiance extraction and contribute to improving the accuracy of interior style detection. Compared to the conventional method, the accuracy of the proposed method increased by 0.128, which was 0.021 higher than ResNet.