## 6 Results and Discussion  
### Accuracy  
Tables 4 and 5 respectively report the relative performance in terms of ranking (as defined in Section 5) of the ten best hand-crafted descriptors and ten best CNN-based features. The results depict a scenario, which, on the whole, was dominated by CNN-based methods. Among them, the three ResNet outperformed by far the other networks, and interestingly, the FC configuration emerged as the best strategy to extract CNN-based features among the three considered (FC, BoVW and VLAD). Conversely, the hand-crafted descriptors came lower in the standings and were dominated by colour variants of LBP (e.g., IOCLBP, OCLBP and LCVBP). It is also most informative to analyse the results by experiment and dataset as reported in Tables 6-16. For each experiment, the corresponding table shows the average accuracy attained by the \begin{table}
\begin{tabular}{l c 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}{*}{IOCLBP (**9**)} & 64.0 & 55.0 & 54.0 & 52.0 & 64.5 & 63.0 & 48.5 & 66.0 & 49.0 & 30.0 & 54.6 & \multirow{2}{*}{**9**} \\  & (91.9) & (78.6) & (84.4) & (71.6) & (96.8) & (82.4) & (74.0) & (71.7) & (67.9) & (18.6) & (73.8) & \\ \hline \multirow{2}{*}{OCLBP (**14**)} & 63.0 & 54.0 & 50.0 & 34.5 & 68.0 & 61.0 & 44.0 & 64.5 & 44.0 & 20.0 & 50.3 & **14** \\  & (91.5) & (77.3) & (81.8) & (68.1) & (97.2) & (80.3) & (72.0) & (70.2) & (66.3) & (16.7) & (72.1) & \\ \hline \multirow{2}{*}{ICM\({}^{DFT}\)} & 61.0 & 47.0 & 30.5 & 25.0 & 64.5 & 57.5 & 51.0 & 61.0 & 38.0 & 21.0 & 45.6 & \multirow{2}{*}{**17**} \\  & (90.6) & (76.9) & (73.5) & (61.4) & (96.5) & (76.4) & (74.9) & (68.0) & (63.5) & (16.9) & (69.9) & \\ \hline \multirow{2}{*}{LCVBP} & 60.0 & 59.5 & 42.0 & 53.5 & 53.0 & 59.0 & 12.5 & 42.5 & 43.0 & 23.0 & 44.8 & \multirow{2}{*}{**18**} \\  & (91.1) & (82.1) & (77.4) & (70.0) & (93.6) & (77.8) & (57.1) & (60.6) & (66.1) & (17.1) & (69.3) & \\ \hline \multirow{2}{*}{Full-Hist-10} & 46.0 & 53.0 & 11.0 & 26.0 & 45.0 & 67.0 & 61.5 & 67.5 & 40.0 & 11.0 & 42.8 & \multirow{2}{*}{**21**} \\  & (83.1) & (57.8) & (56.2) & (64.4) & (92.5) & (84.4) & (61.1) & (73.3) & (63.3) & (14.4) & (68.8) & \\ \hline \multirow{2}{*}{ILBP} & 49.0 & 35.0 & 51.0 & 60.0 & 52.0 & 47.5 & 22.0 & 45.5 & 26.0 & 36.0 & 42.4 & \multirow{2}{*}{**22**} \\  & (86.8) & (70.6) & (81.5) & (69.9) & (94.2) & (71.6) & (60.9) & (61.4) & (59.5) & (21.0) & (67.7) & \\ \hline \multirow{2}{*}{ICM} & 55.0 & 43.0 & 21.0 & 18.0 & 61.0 & 53.5 & 50.0 & 60.0 & 37.0 & 12.5 & 41.1 & \multirow{2}{*}{**23**} \\  & (90.1) & (75.9) & (71.2) & (59.0) & (95.0) & (73.4) & (73.6) & (67.4) & (63.0) & (15.0) & (68.4) & \\ \hline \multirow{2}{*}{LBP + LCC} & 56.5 & 42.0 & 43.0 & 58.5 & 49.0 & 53.5 & 18.0 & 40.0 & 29.0 & 17.5 & 40.7 & \multirow{2}{*}{**25**} \\  & (90.5) & (74.5) & (78.4) & (69.7) & (94.0) & (72.9) & (58.4) & (59.3) & (59.9) & (16.6) & (67.4) & \\ \hline \multirow{2}{*}{SIFT-BoVW} & 38.0 & 44.5 & 60.0 & 62.0 & 19.0 & 20.5 & 27.0 & 53.0 & 31.0 & 46.0 & 40.1 & \multirow{2}{*}{**27**} \\  & (84.5) & (76.4) & (87.1) & (72.4) & (86.1) & (59.4) & (64.1) & (64.4) & (61.6) & (31.6) & (68.8) & \\ \hline \multirow{2}{*}{CLBP} & 47.0 & 38.0 & 36.0 & 53.5 & 47.0 & 51.5 & 21.0 & 37.5 & 32.0 & 35.0 & 39.9 & \multirow{2}{*}{**28**} \\  & (87.8) & (72.4) & (75.5) & (66.9) & (93.6) & (72.5) & (59.3) & (88.1) & (59.9) & (20.6) & (66.7) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hand-crafted descriptors: relative performance of the best ten methods at a glance. For each method, the columns from #1–#10 show the rank (first row) and average accuracy (second row, in parentheses) by experiment. The next to last column reports the average rank and accuracy over all the experiments and the last column the overall position in the placings.