## 6 Results and Discussion
### Computational Demand
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{6}{c}{_Hand-Crafted Image Descriptors_} & \multicolumn{6}{c}{_CNN-Based Features_} \\ \hline Abbreviation & FE & Q\({}_{FE}\) & CL & Q\({}_{CL}\) & Abbreviation & FE & Q\({}_{FE}\) & CL & Q\({}_{CL}\) \\ \hline _Purely spectral_ & & & & & Caffe-Alex-FC & 0.089 & I & 0.611 & III \\ Mean & 0.043 & I & 0.007 & I & Caffe-Alex-BoVW & 0.151 & I & 0.676 & IV \\ Mean + Std & 0.054 & I & 0.007 & I & Caffe-Alex-VLAD & 0.101 & I & 0.626 & IV \\ Mean + Moms & 0.152 & I & 0.009 & I & Caffe-Alex-BoVW & 0.151 & I & 0.676 & IV \\ Quartiles & 0.063 & I & 0.007 & I & DenseNet-161-FC & _1.060_ & * & _2.565_ & * \\ Marg.-Hists-256 & 0.103 & I & 0.114 & II & DenseNet-161-BoVW & _1.462_ & * & _2.647_ & * \\ Full-Hist-10 & 0.168 & II & 0.147 & II & DenseNet-201-FC & _0.858_ & * & _0.924_ & * \\ \hline _Grey-scale texture_ & & & & DenseNet-201-BoVW & _0.986_ & _* & _0.973_ & * \\ CLBP & 0.564 & II & 0.053 & II & GoogleNet-FC & 0.717 & III & 0.146 & II \\ GLBP & 0.272 & II & 0.021 & I & GoogleNet-BoVW & 0.709 & III & 0.150 & II \\ ILBP & 0.329 & II & 0.036 & I & ResNet-50-FC & 0.736 & III & 0.285 & III \\ LBP & 0.267 & II & 0.021 & I & ResNet-50-BoVW & 0.800 & III & 0.333 & III \\ LTP & 0.668 & III & 0.036 & I & ResNet-101-FC & 1.416 & III & 0.289 & III \\ TS & 0.606 & III & 0.406 & III & ResNet-101-BoVW & 1.461 & IV & 0.338 & III \\ GLCM & 0.789 & III & 0.014 & I & ResNet-152-FC & 2.068 & IV & 0.285 & III \\ GLCM\({}^{DFT}\) & 0.812 & III & 0.015 & I & ResNet-152-BoVW & 2.112 & IV & 0.332 & III \\ Gabor & 1.396 & III & 0.016 & I & VGG-Face-FC & 0.381 & II & 0.614 & III \\ Gabor\({}_{cn}\) & 1.455 & IV & 0.016 & I & VGG-Face-BoVW & 0.459 & II & 0.699 & IV \\ Gabor\({}^{DFT}_{cn}\) & 1.395 & III & 0.016 & I & VGG-Face-VLAD & 0.383 & II & 0.620 & IV \\ Gabor\({}^{DFT}_{cn}\) & 1.457 & IV & 0.016 & I & VGG-F-FC & 0.084 & I & 0.614 & III \\ IPBC-J & 4.300 & IV & 0.681 & IV & VGG-F-BoVW & 0.133 & I & 0.687 & IV \\ HCG & 0.142 & I & 0.115 & II & VGG-V-VLAD & 0.092 & I & 0.612 & III \\ VZ-MR8 & 4.366 & IV & 0.679 & IV & VGG-M-FC & 0.160 & I & 0.614 & III \\ SIFT-BoVW & 10.064 & IV & 0.676 & IV & VGG-M-BoVW & 0.223 & II & 0.689 & IV \\ SIFT-VLAD & 0.617 & III & 0.612 & III & VGG-M-VLAD & 0.154 & I & 0.613 & III \\ WSF + WCF\({}^{(1)}\) & 0.953 & III & 0.018 & I & VGG-S-FC & 0.147 & I & 0.616 & IV \\ WSF + WCF\({}^{(2)}\) & 1.018 & III & 0.018 & I & VGG-S-BoVW & 0.269 & II & 0.688 & IV \\ \hline \multicolumn{6}{c}{_Colour texture_} & \multicolumn{6}{c}{VGG-S-VLAD & 0.155 & I & 0.614 & III \\ OCLBP & 1.183 & III & 0.097 & II & VGG-VD-16-FC & 0.382 & II & 0.615 & IV \\ ICCLBP & 1.580 & IV & 0.188 & II & VGG-VD-16-BoVW & 0.463 & II & 0.679 & IV \\ LCVBP & 2.194 & IV & 0.067 & II & VGG-VD-16-VLAD & 0.388 & II & 0.612 & III \\ LBP + LCC & 0.918 & III & 0.130 & II & VGG-VD-19-FC & 0.425 & II & 0.612 & III \\ ICM & 4.495 & IV & 0.057 & II & VGG-VD-19-BoVW & 0.508 & II & 0.679 & IV \\ ICM\({}^{DFT}\) & 4.508 & IV & 0.056 & II & VGG-VD-19-VLAD & 0.437 & II & 0.611 & III \\ OppGabor & 5.427 & IV & 0.094 & II & & & & & \\ OppGabor\({}_{cn}\) & 5.614 & IV & 0.094 & II & & & & & \\ OppGabor\({}^{DFT}_{cn}\) & 5.440 & IV & 0.094 & II & & & & & \\ OppGabor\({}^{DFT}_{cn}\) & 5.617 & IV & 0.095 & II & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Computational demand: FE = average Feature Extraction time per image, CL = average Classification time per problem; Q\({}_{FE}\) and Q\({}_{CL}\) are the corresponding quartiles. Values are in seconds. Note: features’ extraction and classification from the DenseNet-161 and DenseNet-201 models were carried out on a machine different from the one used for all the other descriptors. Consequently, computing times for DenseNets are not directly comparable to those of the other descriptors.