## 6 Results and Discussion
### Accuracy
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{3}{c}{**Dataset**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-3} \cline{5-5}  & **1** & **2** & **3** & **4** \\ \hline \multicolumn{5}{l}{_Hand-crafted image descriptors_} \\ \hline Full-Hist-10 & **89.1** & 76.0 & 78.1 & 81.1 \\ Marginal-Hist-256 & 76.0 & 70.9 & 81.7 & 76.2 \\ Mean + Std & 77.4 & 69.7 & 74.9 & 74.0 \\ ICM\({}^{DFT}\) & 65.2 & 79.3 & 80.2 & 74.9 \\ ICM & 64.0 & 77.7 & 79.1 & 73.6 \\ \hline \multicolumn{5}{l}{_CNN-based features_} \\ \hline ResNet-101-FC & 84.3 & **91.5** & 82.1 & 86.0 \\ ResNet-152-FC & 85.7 & 89.0 & 81.9 & 85.5 \\ ResNet-50-FC & 81.7 & 88.9 & **82.8** & 84.5 \\ VGG-VD-19-FC & 86.8 & 87.3 & 76.7 & 83.6 \\ VGG-VD-16-FC & 87.1 & 86.2 & 76.2 & 83.2 \\ \hline \end{tabular}
\end{table} 
Table 13: Results of Experiment #7: stationary textures with variation in scale. Figures report overall accuracy by dataset. Boldface denotes the highest value; underline signals a statistically-significant difference between the best hand-crafted and the best CNN-based descriptor. Descriptors are listed in ascending order of by-experiment rank (best first). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{3}{c}{**Dataset**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-3} \cline{5-5}  & **1** & **2** & **3** & **4** \\ \hline \multicolumn{5}{l}{_Hand-crafted image descriptors_} \\ \hline CCLBP & 94.9 & 100.0 & 99.2 & **94.6** & 97.2 \\ IOLLBP & 94.9 & 100.0 & 98.6 & 93.8 & 96.8 \\ ICM\({}^{DFT}\) & 95.6 & 100.0 & **99.4** & 91.2 & 96.5 \\ ICM & 93.6 & 100.0 & 99.0 & 87.6 & 95.0 \\ LCVBP & 90.4 & 99.9 & 98.1 & 86.0 & 93.6 \\ \hline \multicolumn{5}{l}{_CNN-based features_} \\ \hline ResNet-101-FC & 98.4 & 100.0 & 98.3 & 86.9 & 95.9 \\ ResNet-50-FC & **98.7** & 100.0 & 98.0 & 87.6 & 96.1 \\ DenseNet-161-FC & 97.0 & 100.0 & 99.1 & 89.0 & 96.3 \\ VGG-S-VLAD & 96.8 & 100.0 & 98.3 & 81.0 & 94.0 \\ VGG-S-FC & 97.4 & 100.0 & 97.4 & 81.3 & 94.0 \\ \hline \end{tabular}
\end{table} 
Table 11: Results of Experiment #5: stationary textures with rotation. Figures report overall accuracy by dataset. Boldface denotes the highest value; underline signals a statistically-significant difference between the best hand-crafted and the best CNN-based descriptor. Descriptors are listed in ascending order of by-experiment rank (best first). \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{3}{c}{**Dataset**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-3} \cline{5-5}  & **1** & **2** & **3** \\ \hline \multicolumn{5}{l}{_Hand-crafted image descriptors_} \\ \hline Full-Hist-10 & **89.1** & 76.0 & 78.1 & 81.1 \\ Marginal-Hist-256 & 76.0 & 70.9 & 81.7 & 76.2 \\ Mean + Std & 77.4 & 69.7 & 74.9 & 74.0 \\ ICM\({}^{DFT}\) & 65.2 & 79.3 & 80.2 & 74.9 \\ ICM & 64.0 & 77.7 & 79.1 & 73.6 \\ \hline \multicolumn{5}{l}{_CNN-based features_} \\ \hline ResNet-101-FC & 84.3 & **91.5** & 82.1 & 86.0 \\ ResNet-152-FC & 85.7 & 89.0 & 81.9 & 85.5 \\ ResNet-50-FC & 81.7 & 88.9 & **82.8** & 84.5 \\ VGG-VD-19-FC & 86.8 & 87.3 & 76.7 & 83.6 \\ VGG-VD-16-FC & 87.1 & 86.2 & 76.2 & 83.2 \\ \hline \end{tabular}
\end{table} 
Table 10: Results of Experiment #5: stationary textures with rotation. Figures report overall accuracy by dataset. Boldface denotes the highest value; underline signals a statistically-significant difference between the best hand-crafted and the best CNN-based descriptor. Descriptors are listed in ascending order of by-experiment rank (best first). Another interesting outcome is the relative performance of the descriptors within the two classes of methods. The ranking of the CNN-based features was rather stable across all the experiments, with the three ResNet models invariably sitting in the first places of the standings.