# Attention Neural Network for Water Image Classification under IoT Environment
## 4 Results and Discussion
### Performance Analysis  
We adopted two related comparison methods in experiments: [PERSON] et al. [33] and [PERSON] et al. [8]. [PERSON] et al. [33] firstly divided input images into sub-regions by angle and spatial features of Fourier spectrum, and then extracted mean and variance features over sub-regions to form feature matrix, which represented texture properties of water images and was further utilized for classification. [PERSON] et al. [8] proposed spatiotemporal feature to detect water surface in inputting sequences, which does not coincide with aim of the proposed method. We utilized their proposed spatiotemporal feature to construct SVM classifier for classification goal. We implemented both methods following the instructions in their recently published papers. Both comparative methods can be applied to perform water image classification task, since the core of their methods are feature representation of visual information of water surface images. Therefore, both methods were adopted as comparative studies to show superiority of feature representation by incorporating context information, which is the main contribution of the proposed network. In fact, simple and manually-designed features are not sufficient to deal with water image classification, due to its complexity and ambiguous characteristics. We thus offer a novel network to not only self-extract feature representation, but also pay attention to the information part after extraction. Compared with the other studies, all these features led to a better accuracy and robustness on task of water image classification. We show quantitative results of classification experiments among the proposed network with and without channel-wise attention module and comparative studies in Table 1. Note that we report measurements on classification for clean water images (four subclasses), polluted water images (six subclasses), and total water images (ten subclasses). To better visualize performance of three classification methods, we also represent confusion matrices for clean, polluted, and total water images in Figures 5-7, respectively. As shown in Table 1 and all the confusion matrix figures, we observed that performance for four clean, six polluted and ten total subclasses achieved by the proposed method (WA) is highest, which shows the proposed network successfully performs water image classification task. High performance \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Clean Water**} & \multicolumn{4}{c}{**Polluted Water**} & \multicolumn{4}{c}{**Total**} \\ \cline{2-10}  & **Precision** & **Recall** & **F-Score** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Accuracy** & **Accuracy** \\ \hline The Proposed (WA) & **0.65** & **0.69** & **0.67** & **66.4\%** & **0.65** & **0.67** & **0.66** & **73.6\%** & **71.2\%** \\ \hline The Proposed (WoA) & **0.65** & **0.69** & **0.67** & 63.2\% & 0.60 & 0.64 & 0.62 & 65.6\% & 69.2\% \\ \hline [PERSON] et al. [33] & 0.52 & 0.52 & 0.52 & 52.0\% & 0.44 & 0.45 & 0.44 & 59.2\% & 51.2\% \\ \hline [PERSON] et al. [8] & 0.30 & 0.30 & 0.30 & 41.6\% & 0.07 & 0.16 & 0.10 & 35.2\% & 20.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of classification performance among the proposed method with attention module (short for WA), without attention module (short for WoA), [PERSON] et al. [33], and [PERSON] et al. [8]. We performed three experiments to show the performance on classification of clean water, polluted water, and the entire dataset. Bold text indicates the best performance among the comparative studies. Figure 5: Classification Confusion matrices for total water images: (**a**) the matrix achieved by the proposed method (WA); (**b**) the matrix achieved by the proposed method (WoA); (**c**) the matrix achieved by [PERSON] et al. [33]; and (**d**) the matrix achieved by [PERSON] et al. [8]. of the proposed method (WA) and (WoA) prove high distinguishability of deep learning structures.