# Learning Part-Based Features for Vehicle Re-Identification with Global Context
## 4 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**SI. No.** & **Method** & \multicolumn{3}{c}{**Rank-1**} & \multicolumn{3}{c}{**Rank-5**} \\ \hline  & & **Small (\%)** & **Medium (\%)** & **Large (\%)** & **Small (\%)** & **Medium (\%)** & **Large (\%)** \\ \hline
1 & SLSR [47] & 75.10 & 71.80 & 68.70 & 89.70 & 86.10 & 83.10 \\ \hline
2 & VANet [48] & 88.12 & 83.17 & 80.35 & 97.29 & 95.14 & 92.97 \\ \hline
3 & Batch Sample [49] & 78.80 & 73.41 & 69.33 & 96.17 & 92.57 & 89.45 \\ \hline
4 & Part regularised near duplicate [25] & 78.40 & 75.00 & 74.20 & 92.30 & 88.30 & 86.40 \\ \hline
5 &
\begin{tabular}{c} MRL + Softmax \\ Loss [50] \\ \end{tabular} & 84.80 & 80.90 & 78.40 & 96.9 & 94.1 & 92.1 \\ \hline
6 & MRM [23] & 76.64 & 74.20 & 70.86 & 92.34 & 88.54 & 84.82 \\ \hline
7 & SAN [27] & 79.7 & 78.4 & 75.6 & 94.3 & 91.3 & 88.3 \\ \hline
8 & TCPM [34] & 81.96 & 78.82 & 74.58 & 96.38 & 94.29 & 90.71 \\ \hline
9 & DGPM [35] & 88.56 & 87.63 & 86.31 & 94.69 & 94.29 & 93.34 \\ \hline
10 & Attribute Net [51] & 86.0 & 81.9 & 79.6 & 97.4 & 95.1 & 92.7 \\ \hline
11 & PGAN [52] & NR & NR & 77.8 & NR & NR & 92.1 \\ \hline
12 & TransReid [54] & 83.6 & NR & NR & 97.1 & NR & NR \\ \hline
13 & VehicleGAN [55] & 83.5 & 78.2 & 75.7 & 96.5 & 93.2 & 90.6 \\ \hline
14 & URRNet [39] & 76.5, & 73.7 & 68.2 & 96.5 & 92.0 & 89.6 \\ \hline
15 & MDFENet [42] & 83.66 & 80.78 & 77.88 & NR & NR & NR \\ \hline
16 & MIMANet [16] & 83.28 & 80.14 & 77.72 & 96.31 & 93.71 & 91.29 \\ \hline
17 & HCI-Net [56] & 83.8 & 79.4 & 76.4 & 96.5 & 92.7 & 91.2 \\ \hline
18 & GLSIPNet + RR (Ours) & 86.26 & 82.15 & 80.12 & 98.26 & 95.92 & 93.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different methods on the VehicleId dataset. NR: not reported. Empirical analysis: The proposed GLSIPNet model rewards parts that are more similar to, or more representative of, the vehicle image while penalizing parts that are dissimilar. In Figure 6, the similarity scores are shown for three vehicle images. The scores are used as weights for the losses of the individual parts to arrive at the total loss over which the entire network is trained. This leads to higher attention on the more relevant parts. We can see in Figure 6 that the model accurately identified regions of more relevance in the vehicle images. This can help in cases of images with occlusion and other interference in patches. The following conclusions can be drawn from the above experimental results:  
* Our proposed model clearly outperforms the baseline, thereby establishing the significance of global-local similarity-induced part learning in addressing the VReID problem using local part-based features. Our method achieves improvements of 2.5% (mAP) on the VeRi dataset and 2.4%, 3.3%, and 2.8% (mAP) on the small, medium, and large variants of the VehicleId dataset, respectively. * Our proposed model also performs better than the other comparison models in terms of mAP on the VeRi dataset and Rank-5 accuracy on the VehicleId dataset. * The models proposed in [51, 52], which outperform our model in Rank-1 and Rank-5 accuracies on the VeRi dataset, fail to do so on the VehicleId dataset. Similarly, the models proposed in [35, 48] perform better in Rank-1 accuracy, but in Rank-5 accuracy on the VeRi dataset, our method performs better. Thus, considering its low complexity and consistent performance, our approach offers a better trade-off than attention-based models with higher complexity. * Our proposed model successfully achieves better results in part-based vehicle re-identification by incorporating a global aspect without adding any additional complexity. This approach is found to be more effective than directly incorporating a global feature branch, as demonstrated by the comparison with SAN [27] in Tables 2 and 3.