# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 4 Experimental Results and Analysis
### Experiments  
To evaluate the proposed SF-SRDA, we compared the related methods with three configurations. The first configuration is the comparison between SF-SRDA and the base-line methods in the coarse-grained partition. The second configuration is the comparison between SF-SRDA and the state-of-arts in the coarse-grained partition. The third configuration is the comparison of the fine-grained partition between SF-SRDA and the base-line methods. In Table 3, features involve the VGG-19(relu5-4) feature, the ResNet-152(pool5) feature and the combination of these two features. We assessed the base-line methods (VGG-19(relu5-4) + SVM, ResNet-152(pool5) + SVM, SFLPP [24], and SRDA [29]) and the proposed SF-SRDA on the visible and IR imagery. Since the SFLPP method can customize the feature dimension after fusion, we found that the fusion feature has the highest accuracy in the 85 dimension after many experiments, so the SFLPP method in Table 3 gives the result when the feature is reduced to 85 dimension. Table 4 shows the train time and test time for different methods. In this experiment, the train images are 873 visible images or 539 IR images, while the test images are 750 visible images or 703 IR images. Figure 4: Some visible image of VAIS dataset. Figure 5: Some infrared image of VAIS dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Feature** & **Dimension** & **Visible** & **IR** \\ \hline \multirow{2}{*}{Single feature+ SVM} & VGG-19(relu5-4) & 100,352 & 86.53\% & 67.71\% \\  & ResNet-152(pool5) & 2048 & 84.93\% & 69.13\% \\ \multirow{2}{*}{SFLPP [24]} & ResNet-152(pool5) + VGG-19(relu5-4) & 85 & 84.93\% & 65.43\% \\  & ResNet-152(pool5) + VGG-19(relu5-4) & 5 & 86.93\% & 70.56\% \\ \multirow{2}{*}{The proposed SF-SRDA} & ResNet-152(pool5) + VGG-19(relu5-4) & 5 & **87.60\%** & **70.98\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between SF-SRDA and the base-line methods in the coarse-grained partition. From Tables 3 and 4, it can be observed that: (1) the dimension of single feature is higher, such as VGG-19(relu5-4) feature, which has 100352 dimension, and the ResNet-152(pool5), which has 2048 dimension. However, the feature has redundancy and single feature recognition ability is insufficient; (2) Comparing SF-SRDA with SFLPP, the feature dimension of fusion using our method is greatly reduced, to only 5 dimensions. Moreover, the recognition rate on visible and IR images is higher than that of the SFLPP method. It shows that SF-SRDA improves the discriminant ability of features; (3) Comparing SF-SRDA with SRDA, the recognition rate of our method is higher than that of SRDA when the feature is reduced to the same dimension. It proves that the structure information between features can be maintained by structure fusion in the process of feature dimension reduction, as is beneficial to target recognition; (4) In terms of training time, our approach is similar to ResNet-152 (pool5) + SVM and SRDA, and much lower than VGG-19 (relu5-4) + SVM and SFLPP; (5) Generally speaking, the proposed SF-SRDA achieves the best results, which can greatly reduce the feature dimension and improve the recognition ability of features to different targets. The VAIS dataset has proposed by the literature [5], which gives experimental results of daytime visible images, daytime IR images, paired visible and IR images, and nighttime IR images. To compare with the method in reference [5], we carried out the vessel classification experiment under the coarse-grained condition according to the setting of reference [5]. Apart from the four test sets mentioned above, we added an all-day IR test set for comparison. In addition, we also compared the proposed SF-SRDA with traditional methods (HOG + SVM, LBP + SVM), SFLPP [24], SRDA [29], and other state-of-the-arts in literature [11; 19; 20].