# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 4 Experiments
### Comparison to the Competitive Methods  
We compare the proposed method TDLBP + WML with three competitive methods, i.e., LBP, BoW and CNN. Note that the experimental results of LBP in this section are the same as the one mentioned in Table 4. For BoW, we stretch a \(9\times 9\) neighborhood around each pixel into an 81 dimensional vector to represent each patch, and apply [PERSON]'s law [45] to normalize the patch vectors. Then, we learn a dictionary for each category by using \(K\)-means clustering [46] over patch vectors, and the size of dictionary for each category is set to 300. Each image is described as a 2100 dimensional vector. Finally, we make use of LIBSVM [47] for SVM training and classification with the radial basis function (RBF) kernel, where the parameters \(C\) and \(\gamma\) are set to 200 and 2, respectively. The \(C\) is a penalty coefficient that trades off the relationship between the misclassification and the complexity of the decision surface. The \(\gamma\) is a parameter of the RBF kernel, and can be seen as the inverse of the radius of influence of samples selected by the model as support vectors. For CNN, we utilize the widely-used VGG-19 model [17] for fine-tuning the network on the cloud datasets. Then we treat the final FC layer as the feature vector. Note that LBP, BoW and CNN utilize the same training samples as TDLBP + WML.  
From the experimental results listed in Table 7, LBP, BoW and CNN are not suitable for multi-view ground-based cloud recognition. However, BoW and CNN still outperform LBP, because LBP is a fixed feature extraction method without learning process. Compared with BoW and CNN, we not only extract the feature vectors from PSMs, but also sufficiently consider the diverse numbers of cloud  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**One View** & **The Other View** & **TDLBP + SMOTEBoost** & **TDLBP + RUSBoost** & **TDLBP + WML** \\ \hline \hline MOC\_e & CAMS\_e & 68.73 & 69.61 & **71.87** \\ MOC\_e & IAP\_e & 73.04 & 73.53 & **76.91** \\ CAMS\_e & IAP\_e & 69.82 & 70.64 & **73.58** \\ CAMS\_e & MOC\_e & 71.86 & 72.37 & **74.65** \\ IAP\_e & CAMS\_e & 68.15 & 68.92 & **72.84** \\ IAP\_e & MOC\_e & 73.68 & 74.85 & **78.52** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Multi-view cloud recognition accuracies (%) comparing the proposed TDLBP + WML with TDLBP + SMOTEBoost and TDLBP + RUSBoost.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**One View** & **The Other View** & **LBP + WML** & **DLBP + WML** & **TDLBP + WML** \\ \hline \hline MOC\_e & CAMS\_e & 38.15 & 70.21 & **71.87** \\ MOC\_e & IAP\_e & 48.39 & 74.36 & **76.91** \\ CAMS\_e & IAP\_e & 38.26 & 71.41 & **73.58** \\ CAMS\_e & MOC\_e & 44.05 & 73.73 & **74.65** \\ IAP\_e & CAMS\_e & 40.27 & 70.65 & **72.84** \\ IAP\_e & MOC\_e & 49.68 & 77.93 & **78.52** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multi-view cloud recognition accuracies (%) comparing the proposed method with LBP + WML and DLBP + WML.  
images in each category. Hence, the proposed method outperforms BoW and CNN by more than 30% and 17%, respectively.