## 3 Results
Conversely, the BoVW-based approach exhibited a higher correlation than the other methods for Target 1; however, the differences in the correlation values among the methodologies were not significant. Overall, considering the precision, average overlap area ratio, and the correlation between similarity and overlap, both the proposed methodology and the NoM-based approach consistently outperformed the BoVW-based approach (Figure 12). Therefore, in terms of accuracy, the proposed methodology and the NoM-based approach appear to be relatively superior. In terms of processing speed, the BoVW-based approach, proposed methodology, and NoM-based approach demonstrated efficient performance in that order. The processing speed was measured as the average time required to determine the overlapping image set for each target image in the dataset, with Dataset 1 including Targets 1 and 2, and Dataset 2 including Targets 3 and 4. In Dataset 1, the NoM-based approach required approximately ten times more processing time than the BoVW-based approach (1.51 s) \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Target**} & \multicolumn{3}{c}{**Correlation (Absolute Value)**} \\ \cline{2-4}  & **NoM** & **BoVW** & **Proposed** \\ \hline
1 & 0.81 & 0.85 & 0.80 \\
2 & 0.87 & 0.50 & 0.87 \\
3 & 0.83 & 0.58 & 0.87 \\
4 & 0.87 & 0.21 & 0.97 \\ Average & 0.84 & 0.53 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of correlation among proposed method, NoM, and BoVW-based approaches. Figure 11: Image set selection results for Target 2 using the BoVW-based method (red borders: images that do not overlap, yellow borders: images that overlap with the target image). Figure 12: Comparison among proposed, NoM-based, and BoVW-based methods: (**a**) Precision; (**b**) average overlap area ratio; (**c**) correlation. and the proposed methodology (1.72 s), with an average of 15.76 s per image (Table 8, Figure 13). Conversely, in Dataset 2, which had a relatively higher resolution, all three methods exhibited increased average processing times compared to Dataset 1. The BoVW-based approach and the proposed methodology demonstrated average processing times of 4.13 s and 7.78 s per image, respectively. This represents a reduction in the processing time of 0.06 and 0.11 times compared to the NoM-based approach's average processing time of 73.40 s. Overall, the processing times of the BoVW-based approach and proposed methodology were significantly lower than those of the NoM-based approach, indicating superior performance in terms of processing speed. The bundle adjustment results for the overlapping image sets indicate that the proposed method and NoM-based approach demonstrate a relatively higher accuracy than the BoVW-based approach. Table 9 presents the bundle adjustment accuracy results for the overlapping image sets for each method. As the size of the image sets was standardized, the processing time for bundle adjustment was consistent across all methods. The standard deviation of the EOP, representing precision, ranged from a maximum of 0.200 m to a minimum of 0.053 m for the proposed method and from a maximum of 0.217 m to a minimum of 0.044 m for the NoM-based approach, indicating comparable levels. This was higher than that of the BoVW-based approach, which ranged from a maximum of 0.693 m to a minimum of 0.153 m. Similarly, the RMS residual was approximately 0.750 on average for the proposed method and 0.830 for the NoM-based approach, which was higher than that of the BoVW-based approach, which showed an average of 1.558. During the bundle adjustment process, some nonoverlapping images included in the image set were automatically excluded. Generally, a greater number of images were successfully utilized in bundle adjustment with the proposed methodology and the NoM-based approach than with the BoVW-based approach. However, in the case of Target 2, the BoVW approach yielded 18 successful images, which was more than the 16 images obtained using the proposed methodology and the NoM-based approach.