## 4 Mosquito Species Classification Experiment
### Experimental Results  
Tables 2 and 3 show the aggregated accuracies over the three considered species of conventional and deep classifications, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Accuracy [\%]** & **Features** & **Accuracy [\%]** \\ \hline SURF & 81.6 & LBP & 57.3 \\ \hline SIFT & 82.4 & RGB & 30.7 \\ \hline Dense SIFT & 55.9 & HSV & 37.4 \\ \hline HOG & 54.7 & L*a*b* & 33.7 \\ \hline CoHOG & 57.0 & GLCM & 53.6 \\ \hline ECoHOG & 61.1 & GIST & 71.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of conventional classification. Speeded-up robust features (SURF); histogram of oriented gradient (HOG); co-occurrence HOG (CoHOG); extended CoHOG (ECoHOG); local binary pattern (LBP); red, green, blue (RGB); hue, saturation, value (HSV); gray level co-occurrence matrix (GLCM); GIST. L*a*b*.  
Figure 6: Examples of data argumentation.  
In conventional classification, parameters for features were set to default values adopted in OpenCV libraries on Python. The hyperparameters of SVM were determined based on preliminary experiments. The error-term penalty parameter, \(C\), was set to one and the kernel function was defined as a linear kernel. The SVM classifier with these parameters had the highest accuracy in the grid search.  
The classification accuracies with SIFT and SURF are high. By applying the fact that the classification with dense SIFT exhibits low accuracy, it turns out that an algorithm that detects feature points and describes local features is effective.  
The deep classification has a lower classification accuracy than the conventional classification unless it is by data augmentation. By applying data augmentation, the deep classification has higher accuracy than the conventional classification. Therefore, data augmentation is effective. ResNet has the highest discrimination accuracy of 95.5%, indicating that deep classification is effective for mosquito species classification.  
Figure 7 shows the confusion matrices. Figure 6(a) shows one from the conventional classification with SIFT, which achieved the highest accuracy in Table 2. Each species is misclassified from each other in comparison with the deep classification result.  
Figure 6(b) shows one from the deep classification with ResNet, which achieved the highest accuracy in Table 3. This matrix was calculated from the result of one training and testing phase among five ones. _Aedes albopictus_ and _Culex pipiens pallens_ are relatively misclassified as each other, whereas _Anopheles stefensi_ is relatively classified appropriately.  
Figure 8 shows an image visualizing the target spot in gradient-weighted class activation mapping (Grad-CAM), which is a visualization method [23] for deep classification. The heatmap covers the body of the mosquito.  
\begin{table}
\begin{tabular}{c|c|c} \hline \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**Accuracy [\%]**} \\ \cline{2-3}  & \multicolumn{2}{c}{**Data Augmentation**} \\ \hline  & & ✓ \\ \hline AlexNet & 69.8 & 92.3 \\ \hline VGGNet & 81.2 & 91.0 \\ \hline ResNet & 74.4 & 95.5 \\ \hline \end{tabular}
\end{table}
Table 3: Result of deep classification. AlexNet; Visual Geometry Group Network (VGGNet); Deep residual network (ResNet).  
Figure 7: Example results of the confusion matrices.  
_Aedes albopictus_ and _Culex pipiens pallens_ have a similar shape but their body colors are different. Nevertheless, _Aedes albopictus_ and _Culex pipiens pallens_ can be classified based on similar body parts from the visualization result. The images captured using a smartphone do not indicate the color differences when the shooting environment is dark because a smartphone camera is unable to offer a quality shot when compared with the single-lens reflex cameras. The above facts lead to misclassification.