## 6 Results and Discussion
### Accuracy
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). Under variable illumination conditions, CNN-based descriptors seemed to be able to compensate for changes in illumination better than hand-crafted descriptors did (Experiments #3 and #4, Tables 9-10). This result is in agreement with the findings of [PERSON] et al. [57]. The results were however rather split in the presence of rotation (Experiments #5 and #6, Tables 11 and 12). Here, the hand-designed descriptors were significantly better than CNN-based features in three datasets out of six, whereas the reverse occurred in two datasets. This parallels the results reported in [1] and is likely to be related to the directional nature of the learned filters in the convolutional networks. Figure 14: Hand-crafted image descriptors proved generally better than CNN-based features for the classification of stationary textures acquired under invariable imaging conditions. The gap was substantial when it came to discriminating between very similar textures, as those shown in the picture. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{5}{c}{**Dataset**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-2} \cline{4-7}  & **1** & & **2** & **3** & **4** & **5** \\ \hline \multicolumn{7}{l}{_Hand-crafted image descriptors_} \\ \hline SIFT-BoVW & 77.9 & 79.0 & 95.1 & 97.5 & 86.2 & 87.1 \\ IOCLBP & 82.0 & **85.9** & 91.7 & 82.8 & 79.5 & 84.4 \\ IIPC-J & 70.5 & 85.7 & 89.6 & 94.2 & 71.6 & 82.3 \\ VZ-MR8 & 73.3 & 69.8 & 94.1 & 96.4 & 82.0 & 83.1 \\ ILBP & 76.9 & 84.2 & 80.8 & 96.4 & 69.0 & 81.5 \\ \hline \multicolumn{7}{l}{_CNN-based features_} \\ \hline ResNet-50-FC & **96.0** & 83.0 & **99.1** & 98.8 & **97.2** & 94.8 \\ ResNet-101-FC & 95.6 & 81.6 & 98.9 & **98.9** & 97.0 & 94.4 \\ ResNet-152-FC & 95.5 & 81.9 & 98.8 & 98.3 & 95.9 & 94.1 \\ VGG-VD-19-FC & 93.4 & 76.4 & 96.8 & 97.5 & 94.4 & 91.7 \\ VGG-VD-16-FC & 93.9 & 76.1 & 97.2 & 97.2 & 93.9 & 91.6 \\ \hline \hline \end{tabular}  
* Datasets: 1) ALOT-95-S-I; 2) Outex-192-S-I; 3) RawFoot7-68-S-I-1; 4) RawFoot7-68-S-I-2; 5) RawFoot7-68-S-I-3.  
\end{table} 
Table 9: Results of Experiment #3: stationary textures with variations in illumination. 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). A similar trend emerged with variations in scale (Experiments #7 and #8, Tables 13 and 14). In this case, the hand-designed descriptors (particularly 3D colour histogram) were significantly better than CNN-based features in two datasets, while the reverse occurred in the other two. In the presence of multiple and uncontrolled changes in the imaging conditions, including variations in illumination, scale and viewpoint (Experiments #9 and #10, Tables 15 and 16), the hand-crafted descriptors were just non-competitive: CNN-based features proved superior in all the datasets considered. The difference was more noticeable in those datasets (e.g., RDAD) where the intra-class variability was higher. Particularly interesting were the results obtained with the Describable Texture Dataset: here, CNN-based features surpassed hand-crafted descriptors by \(\approx\)30 percentage points. On the same dataset, the 60.8% accuracy achieved by ResNet-50 was equally remarkable, in absolute terms.