## 6 Results and Discussion
### Accuracy
Conversely, hand-crafted descriptors showed a higher degree of variability: LBP colour variants (e.g., OCLBP, IOLBP, and LCVBP) for instance--which were among the best methods on the whole--did not perform well under variable illumination (as one would expect) and were in fact surpassed by grey-scale methods (e.g., SIFT and ILBP). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{6}{c}{**Dataset**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-2} \cline{4-7}  & **1** & & & **2** & & **3** & **4** & **5** & **6** \\ \hline \hline \multicolumn{7}{l}{_Hand-crafted image descriptors_} \\ \hline IOLBP & 88.7 & 33.8 & 84.1 & 91.7 & 66.9 & 41.9 & 67.9 \\ OCLBP & 89.1 & 30.9 & 82.2 & 90.3 & 64.4 & 41.0 & 66.3 \\ LCVBP & 90.6 & 43.4 & 81.2 & 83.8 & 62.3 & 35.2 & 66.1 \\ Full-Hist-10 & 75.5 & 19.6 & 95.5 & 93.1 & 55.9 & 40.0 & 63.3 \\ OppGabor & 85.1 & 30.7 & 83.6 & 88.9 & 53.6 & 34.4 & 62.7 \\ \hline \multicolumn{7}{l}{_CNN-based features_} \\ \hline ResNet-101-FC & 94.2 & 51.9 & **97.1** & **98.3** & 83.9 & 75.3 & 83.4 \\ ResNet-152-FC & 94.0 & **53.3** & **97.1** & **98.3** & 83.5 & **75.7** & 83.6 \\ ResNet-50-FC & 94.1 & 52.6 & 95.8 & 98.1 & **84.2** & 75.0 & 83.3 \\ VGG-VD-19-FC & 91.0 & 40.6 & 96.5 & 97.2 & 80.4 & 73.2 & 79.8 \\ VGG-VD-16-FC & 90.7 & 39.2 & 96.2 & 97.0 & 81.4 & 73.3 & 79.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Results of Experiment #9: stationary textures with multiple variations. 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} \hline \hline \multirow{2}{*}{**Descriptor**} & **Dataset** \\ \cline{2-2} \cline{4-5}  & **1** \\ \hline \hline \multicolumn{5}{l}{_Hand-crafted image descriptors_} \\ \hline Full-Hist-10 & **73.3** \\ Marginal-Hist-256 & 73.2 \\ IOLBP & 71.7 \\ OCLBP & 70.2 \\ ICM\({}^{DFT}\) & 68.0 \\ \hline \hline \multicolumn{5}{l}{_CNN-based features_} \\ \hline ResNet-50-FC & 70.4 \\ ResNet-101-FC & 69.2 \\ ResNet-152-FC & 69.5 \\ VGG-VD-16-FC & 66.4 \\ VGG-VD-19-FC & 65.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of Experiment #8: non-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} \hline \hline \multirow{2}{*}{**Descriptor**} & **Dataset** \\ \cline{2-3}  & **1** \\ \hline \multicolumn{2}{l}{_Hand-crafted image descriptors_} \\ \hline SIFT-BoVW & **31.6** \\ VZ-MR8 & 27.8 \\ SIFT-VLAD & 27.1 \\ IPBC-J & 22.7 \\ ILBP & 21.0 \\ \hline \multicolumn{2}{l}{_CNN-based features_} \\ \hline ResNet-50-FC & **60.8** \\ ResNet-152-FC & 60.4 \\ ResNet-101-FC & 60.3 \\ VGG-VD-19-FC & 56.2 \\ VGG-VD-16-FC & 56.2 \\ \hline \multicolumn{2}{l}{Datasets: 1) DTD-47-NS-M.} \\ \hline \end{tabular}
\end{table} 
Table 16: Results of Experiment #10: non-stationary textures with multiple variations. 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). Figure 16: Number of datasets on which: hand-crafted methods performed significantly better than CNNs (“HC > CNN”); there was no significant difference (“No diff.”) and CNNs performed significantly better than hand-crafted methods (“CNN > HC”). see also Tables 6–13.