# View Synthesis with Scene Recognition for Cross-View Image Localization
## 4 Performance Evaluation for View Synthesis
### Evaluation Using Jain Index
\"Sub.\" means subjective ruling, and \"Str.\" means strict ruling. Table 5 shows the average accuracy when five attributes are selected from the image. In Figure 6, the data points are broken into three different colors and four different shapes. The data points in red come from the mountain model, the data points in green come from the church model, and the data points in blue come from the bedroom model. Then, the data points with the circle symbol are from the overcast image group, the data points with the star symbol are from the forest image group, the data points with the diamond symbol are from the cloudy image group, and the data points with the square symbol are from the road image group. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**Cloudy**} & \multicolumn{2}{c}{**Forest**} & \multicolumn{2}{c}{**Overcast**} & \multicolumn{2}{c}{**Roads**} \\ \cline{2-9}  & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** \\ \hline Bedroom & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% \\ Church & 15\% & 15\% & 5\% & 0\% & 10\% & 10\% & 8.33\% & 8.33\% \\ Mountain & 10\% & 5\% & 5\% & 3.33\% & 8.33\% & 1.67\% & 0\% & 0\% \\ \hline \end{tabular}
\end{table} 
Table 4: Average accuracy when only one attribute is selected from each image. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Cloudy**} & \multicolumn{2}{c}{**Forest**} & \multicolumn{2}{c}{**Overcast**} & \multicolumn{2}{c}{**Roads**} \\ \cline{2-9}  & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** & **Sub.** \\ \hline Bedroom & 5\% & 5\% & 28.33\% & 11.67\% & 1.67\% & 5\% & 5\% & 28.33\% \\ Church & 33.33\% & 31.67\% & 50\% & 28.33\% & 20\% & 33.33\% & 31.67\% & 50\% \\ Mountain & 41.67\% & 41.67\% & 40\% & 40\% & 46.67\% & 41.67\% & 41.67\% & 40\% \\ \hline \end{tabular}
\end{table} 
Table 5: Average accuracy when five attributes are selected from each image. From the Jain index results from all the images that were tested, the results that were the closest to 1.0 showed the image had an even distribution of attributes from both patch images from the Flickr dataset and reference images from Google Street View. Results that had a Jain index result of 0.5 or less showed an uneven distribution of attributes from both Flickr and Google Street View. Based on the results, the mountain model created more images with a Jain index closer to 1.0 than the church and bedroom models. In contrast, the bedroom model performed the worst out of the three models by having the most images with a Jain index result of 0. The church model had the most images with a Jain index of 0.5, and if the model was trained more the church model would have an opportunity to perform just as well as the mountain model. The discussion on each pre-trained model is as follows:  
1. Mountain Model. The mountain model had the most trouble with the forest image group. The mountain model also performed very well with the overcast and cloudy image group. Overall, the mountain model with more training could achieve higher Jain index results. However, since the model was mostly trained on images consisting of different mountains, the model would struggle to adapt to images with many buildings. 2. Church Model. The church model had trouble with the overcast image group. All the overcast images only had a Jain index of 0.5. With more training, the church model could achieve results similar to the mountain model. However, the church model was primarily trained on buildings, so any images that do not have many buildings would pose a problem for the church model. 3. Bedroom Model. The bedroom model struggled with all the image groups. Only a few of the images had a Jain index of 1. Moreover, even fewer images had a Jain index of 0.5, and many images had a Jain index of 0. However, this was not too surprising since the model was trained on images based on indoor scenes and features. Therefore, it was not a good fit to transfer styles for reference images that were mostly outdoor images.