## 6 Results and Discussion
### Accuracy
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{8}{c}{**Rank (by Experiment)**} & \multirow{2}{*}{**Avg. **} & \multirow{2}{*}{**Overall Position**} \\ \cline{2-11}  & \#1 & \#2 & \#3 & \#4 & \#5 & \#6 & \#7 & \#8 & \#9 & \#10 \\ \hline \multirow{2}{*}{ResNet-50-FC} & 68.0 & 68.0 & 68.0 & 66.0 & 68.0 & 66.0 & 64.5 & 66.0 & 68.0 & 67.0 & \multirow{2}{*}{**1**} \\  & (91.4) & (87.9) & (94.8) & (84.8) & (96.1) & (84.6) & (84.5) & (70.4) & (83.3) & (60.8) & (83.9) & \\ \hline \multirow{2}{*}{ResNet-101-FC} & 66.0 & 67.0 & 67.0 & 67.0 & 67.0 & 65.5 & 68.0 & 62.0 & 67.5 & 66.5 & \multirow{2}{*}{**66.3**} \\  & (00.5) & (77.1) & (94.4) & (84.0) & (95.9) & (83.9) & (86.0) & (69.2) & (83.4) & (60.3) & (83.5) & \\ \hline \multirow{2}{*}{ResNet-152-FC} & 65.0 & 66.0 & 66.0 & 66.0 & 62.0 & 65.5 & 67.0 & 63.0 & 67.5 & 66.5 & \multirow{2}{*}{**3**} \\  & (90.4) & (87.0) & (94.1) & (83.7) & (95.7) & (83.9) & (85.5) & (69.5) & (83.6) & (60.4) & (83.4) & \\ \hline \multirow{2}{*}{VGG-VD-16-FC} & 54.0 & 64.0 & 64.0 & 64.0 & 58.0 & 62.0 & 64.0 & 59.0 & 64.0 & 64.5 & 61.8 & \multirow{2}{*}{**4**} \\  & (88.4) & (83.3) & (91.6) & (81.3) & (94.1) & (82.2) & (83.2)best five hand-crafted image descriptors and the best five CNN-based features (the other values are provided as Supplementary Material). Experiments #1 and #2 (Tables 6-8) show that, under steady imaging conditions, hand crafted-descriptors were competitive only with stationary textures, whereas CNN-based features proved clearly superior with non-stationary ones (Figures 13-15). In Experiment #1 (Tables 6 and 7), the best-performing method belonged to the first group in the four datasets out of 14; the reverse occurred in eight datasets, whereas in the remaining two, the difference did not reach statistical significance (Figure 16). Interestingly, there was a marked gap when it came to classifying fine textures \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{5-8}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline _Hand-crafted_ & & & & & & & \\ \hline IOLBP & 96.7 & 99.5 & 98.7 & **100.0** & 83.6 & 98.4 & **95.3** & 91.9 \\ OCLBP & 96.0 & 99.5 & 96.6 & **100.0** & 85.3 & 99.1 & 95.2 & 91.5 \\ ICM\({}^{DT}\) & 95.8 & 99.0 & 94.2 & **100.0** & 95.7 & 99.2 & 91.7 & 90.6 \\ LCVBP & 92.4 & 97.7 & 85.7 & **100.0** & **97.4** & 98.0 & 88.2 & 91.1 \\ LBP + LCC & 94.4 & 97.3 & 89.7 & 99.7 & 90.5 & 98.1 & 87.4 & 90.5 \\ \hline _CNN-based_ & & & & & & & \\ \hline ResNet-50-FC & 98.7 & **100.0** & 87.8 & **100.0** & 96.6 & 99.0 & 90.6 & 91.4 \\ DenseNet-161-FC & 98.6 & 99.7 & **100.0** & 100.0 & 83.2 & **99.5** & 90.6 & 91.5 \\ ResNet-101-FC & **98.9** & 99.8 & 85.7 & **100.0** & 95.5 & 99.3 & 89.9 & 90.5 \\ ResNet-152-FC & 98.7 & **100.0** & 84.1 & **100.0** & 93.4 & 98.8 & 89.6 & 90.4 \\ VGG-S-FC & 98.6 & 99.3 & 87.4 & **100.0** & 88.3 & 98.7 & 86.4 & 89.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of Experiment #1 (Part 1: Datasets 1–7): stationary textures acquired under steady imaging conditions. 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).