## 3 Results
### Experimental Comparison
This is because the New College dataset has short intervals between image captures, and scenes with loops, mainly involving frequent human movement, indicate a noticeable improvement in loop detection under a high compression rate of KL transformation. In contrast, the City Center dataset has fewer moving objects, and scenes with loops often involve significant occlusions. Therefore, even with a compression rate of 70%, a good detection effect can be achieved. Based on the evaluation method introduced in Section 3.2, we further conducted experiments on the New College and City Center datasets. The goal is to observe, under the condition of 100% accuracy based on Ground Truth, the proportion of correctly detected loops by the ResNet152 network model after undergoing KL transformation. In the New College dataset, the maximum recall rates of different convolutional layers in ResNet152 under various compression ratios are presented in Table 4, while the precision-recall curves of different convolutional layers in ResNet152 under the same compression ratio are shown in Figure 20. It can be observed that, at low compression rates, there is little difference in performance among conv3, conv4, and conv5 of ResNet152. However, at high compression rates, the conv5 layer has more capability to represent features, achieving a maximum recall rate of 60.14% at a compression ratio of 90%. This indicates that, under high compression rates, the conv5 layer can effectively resist interference from moving objects and occlusions, making it suitable for scenes with a large crowd flow. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{4}{c}{**Max Recall at 100\% Precision (\%)**} \\ \cline{2-5}  & \(\gamma=60\%\) & \(\gamma=70\%\) & \(\gamma=80\%\) & \(\gamma=90\%\) \\ \hline Conv1 & 14.62 & 14.23 & 12.69 & 9.41 \\ Conv2 & 14.22 & 13.76 & 18.31 & 16.58 \\ Conv3 & 25.34 & 26.86 & 30.21 & 28.17 \\ Conv4 & 30.67 & 28.52 & 40.07 & 45.69 \\ Conv5 & 27.88 & 24.39 & 46.55 & 60.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum recall rates of different convolutional layers in ResNet152 under various compression ratios at 100% accuracy in the New College dataset. Figure 19: Similarity-detection results after KL transformation (New College left, City Center right). In the City Center dataset, the maximum recall rates of different convolutional layers in ResNet152 at various compression ratios under the condition of the highest precision (precision = 100%) are presented in Table 5. The accuracy-recall curves for different convolutional layers of ResNet152 at the same compression ratio are illustrated in Figure 21. Compared to the New College dataset, the conv3 layer at 80% compression already achieves around 50% recall, while the conv4 layer performs better at 70% compression, demonstrating feature-representation capability to similar that of the conv5 layer at 90% compression. The experimental results indicate that for different convolutional layers of ResNet152, when there are numerous occlusions in the scenes belonging to loops, a lower compression ratio (\(\gamma\) = 60% or 70%) is preferable for compressing the outputs of the conv3 and conv4 layers. Conversely, when there are abundant moving objects in scenes associated with loops, a higher compression ratio (80% or 90%) is recommended for compressing the features output by the conv5 layer. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{4}{c}{**Max Recall at 100\% Precision (\%)**} \\ \cline{2-5}  & **Ratio = 60\%** & **Ratio = 70\%** & **Ratio = 80\%** & **Ratio = 90\%** \\ \hline Conv1 & 33.64 & 39.72 & 37.88 & 29.17 \\ Conv2 & 38.72 & 47.38 & 42.39 & 37.54 \\ Conv3 & 48.13 & 46.75 & 50.21 & 47.68 \\ Conv4 & 62.54 & 71.42 & 69.01 & 63.87 \\ Conv5 & 75.65 & 75.09 & 73.42 & 73.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum recall rates of different convolutional layers in ResNet152 under various compression ratios at 100% accuracy in the City Center dataset.