# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 4 Experiments
### Effect of TDLBP  
We compare the proposed TDLBP with the other two texture features, i.e., LBP and DLBP. It should be noted that we extract LBP from the original cloud images and the PSMs, respectively, so we define the second one as DLBP. For fair comparison, we partition all original cloud images (for LBP) and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cloud Category**} & \multicolumn{3}{c}{**Number of Cloud Images**} \\ \cline{2-4}  & **MOC\_e** & **CAMS\_e** & **IAP\_e** \\ \hline Cumulus & 278 & 397 & 1072 \\ Cirrus and cirrostratus & 303 & 373 & 516 \\ Cirrocumulus and altocumulus & 109 & 113 & 32 \\ Clear sky & 302 & 171 & 88 \\ Stratocumulus & 35 & 188 & 536 \\ Stratus and altostratus & 836 & 192 & 679 \\ Cumulonimbus and nimbostratus & 244 & 1057 & 610 \\ Total number & 2107 & 2491 & 3533 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The sample number in each category of three datasets.  
Figure 5: We present cloud samples of each category (each row indicates one category) from the three cloud datasets, i.e., (**a**) the MOC_e, (**b**) the CAMS_e, and (**c**) the IAP_e.  
PSMs (for DLBP and TDLBP) into \(L\times L\) (\(L\) = 1, 2, 3) regions. For each region, we extract three scales LBP with \((P,R)\) equal to (8, 1), (16, 2) and (24, 3). As for LBP, we accumulate LBP histograms in each divided region, and concatenate all histograms into one histogram with \(1\times 54+4\times 54+9\times 54=756\) dimensions. As for DLBP, within each region of the PSMs, we extract LBP histograms, and then apply sum pooling to aggregate all features in each region. Each image is also described as a feature vector with 756 dimensions. The chi-square metric is used in this section, and Table 3 presents the recognition accuracies.  
From Table 3, in all six situations, the highest classification accuracies are obtained by TDLBP. Both TDLBP and DLBP outperform LBP, because the CNN can learn highly nonlinear features for view shift. Moreover, TDLBP and DLBP are extracted from the PSMs which contain the completed and spatial information of clouds. The TDLBP outperforms DLBP by about 1% in all six situations. Since cloud images have some interferences and noises in general, max pooling could opt for the discriminative and salient features. Hence, TDLBP is more suitable for adapting view shift. Furthermore, the best performance is obtained in the situation of the IAP_e to MOC_e shift. This is probably because the cloud images of IAP_e have some similarities with the ones of MOC_e, such as illuminations, occlusions and locations.  
We replaced chi-square metric with metric learning to classify the cloud images with the three features, and we denote them as LBP + ML, DLBP + ML and TDLBP + ML, respectively. From the results shown in Table 4, with the help of metric learning, the performance improvement is more significant, i.e., it all improves approximately by 2%. Particularly, TDLBP + ML achieves the best recognition results in all six conditions. It demonstrates that TDLBP is effective both in predefined metric and learning-based metric. In addition, it is observed that metric learning is more suitable for measuring the similarity between sample pairs when presented with view shift.