## 6 Results and Discussion
### Accuracy
\begin{table}
\begin{tabular}{l c 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 & 69.0 & 77.7 & 99.2 & 97.3 & 94.2 & 82.1 & **95.4** & 91.9 \\ OCLBP & 69.7 & 76.9 & 98.8 & 96.2 & 92.3 & 83.0 & 93.8 & 91.5 \\ ICM\({}^{DFT}\) & 62.1 & 76.8 & 98.0 & 93.9 & 92.5 & 81.5 & 90.1 & 90.6 \\ LCVBP & **75.1** & 75.4 & 98.4 & 96.6 & 95.2 & 83.7 & 91.5 & 91.1 \\ LBP + LCC & 67.2 & 79.3 & 99.1 & 93.7 & 92.6 & 85.7 & 94.2 & 90.5 \\ \hline _CNN-based_ & & & & & & & \\ \hline ResNet-50-FC & 53.0 & **86.6** & **99.8** & **99.0** & **99.6** & 85.4 & 85.4 & 91.4 \\ DenseNet-161-FC & 69.0 & 75.1 & 99.7 & 97.5 & 96.2 & 82.9 & 88.5 & 91.5 \\ ResNet-101-FC & 51.8 & 82.1 & 99.7 & 98.8 & 99.2 & 85.1 & 83.8 & 90.5 \\ ResNet-152-FC & 56.1 & 83.9 & 99.7 & 98.6 & 99.4 & 83.2 & 82.9 & 90.4 \\ VGG-S-FC & 56.3 & 74.3 & 98.1 & 98.1 & 98.0 & **86.0** & 82.3 & 89.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of Experiment #1 (Part 2: Datasets 8–14): 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). with a high degree of similarity, such as in datasets Parquet-38-S-N and V\(\times\)CTSG-42-S-N (Figure 14). In this case, the hand-crafted descriptors outperformed CNN-based features by a good margin. With non-stationary textures (Experiment #2), CNN-based features proved generally better, outperforming hand-crafted descriptors in six datasets out of nine, whereas the reverse occurred in two datasets only. Figure 13: Relative performance of hand-crafted descriptors vs. CNN-based features with stationary (\(x\) axis) and non-stationary textures (\(y\) axis) under invariable imaging conditions (Experiments #1 and #2, best 13 methods). The plot shows a clear divide between CNN-based methods (mostly clustered in the upper-left part, therefore showing affinity for non-stationary textures) and hand-crafted descriptors (mostly clustered in the lower-right part, therefore showing affinity for stationary textures) \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{8}{c}{**Dataset**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-2} \cline{5-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multicolumn{10}{l}{_Hand-crafted image descriptors_} \\ \hline LCVBP & 75.4 & 75.3 & **89.8** & 85.2 & 77.6 & 88.6 & 95.8 & 69.1 & 82.1 \\ IOCLBP & 78.6 & 79.9 & 54.4 & 80.2 & **84.1** & 90.3 & 95.4 & 66.0 & 78.6 \\ OCLBP & 74.8 & 74.8 & 60.1 & 79.9 & 83.3 & 87.7 & 92.6 & 65.0 & 77.3 \\ Full-Hist-10 & 87.7 & 87.7 & 28.8 & 78.0 & 79.3 & 87.1 & 92.2 & 65.6 & 75.8 \\ Marginal-Hists-256 & 71.0 & 79.6 & 99.6 & 73.7 & 81.5 & 70.5 & 79.6 & 70.3 & 78.2 \\ \hline \multicolumn{10}{l}{_CNN-based features_} \\ \hline ResNet-50-FC & 94.9 & **93.0** & 77.7 & 90.7 & 78.5 & **97.9** & **99.6** & **71.0** & 87.9 \\ ResNet-101-FC & 94.7 & 92.8 & 76.6 & 90.5 & 76.6 & 97.5 & 99.2 & 68.6 & 87.1 \\ ResNet-152-FC & **95.0** & 91.8 & 76.5 & **90.9** & 76.7 & 97.4 & 99.1 & 68.8 & 87.0 \\ VGG-M-FC & 87.7 & 86.8 & 64.1 & 84.3 & 74.5 & 95.1 & 99.2 & 68.3 & 82.5 \\ VGG-VD-16-FC & 93.1 & 85.6 & 71.5 & 79.1 & 76.8 & 96.3 & 96.6 & 67.2 & 83.3 \\ \hline \multicolumn{10}{l}{Datasets: 1) ALOT-40-NS-N; 2) ForestSpecies-112-NS-N; 3) MBT-34-NS-N; 4) NewBarkTex-6-NS-N; 5) Outex-59-NS-N; 6) STex-138-NS-N; 7) USPTex-33-NS-N; 8) VisTex-78-NS-N.} \\ \end{tabular}
\end{table} 
Table 8: Results of Experiment #2: non-stationary textures acquired under steady imaging conditions. Figures report overall accuracy by dataset.