## 4 Methods
### Hand-Designed Descriptors
#### 4.1.2. Grey-Scale Texture Descriptors  
Six LBP variants, also referred to as histograms of equivalent patterns [67], specifically:  
* _Completed Local Binary Patterns_[68] (CLBP, 324 features) \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Pre-Trained Model** & \begin{tabular}{c} **Output Layer** \\ **(No. or Name)** \\ \end{tabular} & \
\begin{tabular}{c} **Aggregation** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Abbreviation** \\ **Features** \\ \end{tabular} \\ \hline DenseNet\_161.caffemodel & “pool” (last Fully-Conn.) & None & DenseNet-161-FC & 2208 \\ DenseNet\_161.caffemodel & “concat\_5.24” (last conv.) & BoVW & DenseNet-161-BoVW & 2208 \\ DenseNet\_201.caffemodel & “pool” (last fully-conn.) & None & DenseNet-201-FC & 1920 \\ DenseNet\_201.caffemodel & “concat\_5.32” (last conv.) & BoVW & DenseNet-201-BoVW & 1920 \\ imagenet-googlenet-dag & “c\_cls\_pool” (last fully-conn.) & None & GoogLeNet-FC & 1024 \\ imagenet-googlenet-dag & “icp\_out” (conv.) & BoVW & GoogLeNet-BoVW & 1024 \\ imagenet-caffe-alex & 20 (last fully-conn.) & None & Caffe-Alex-FC & 4096 \\ imagenet-caffe-alex & 13 (last conv.) & BoVW & Caffe-Alex-BoVW & 4096 \\ imagenet-caffe-alex & 13 (last conv.) & VLAD & Caffe-Alex-VLAD & 4224 \\ imagenet-resnet-50-dag & “pool” (last fully-conn.) & None & ResNet-50-FC & 2048 \\ imagenet-resnet-50-dag & “res5c\_branch2c” (conv.) & BoVW & ResNet-50-BoVW & 2048 \\ imagenet-resnet-101-dag & “pool” (last fully-conn.) & None & ResNet-101-FC & 2048 \\ imagenet-resnet-101-dag & “res5c\_branch2c” (conv.) & BoVW & ResNet-101-BoVW & 2048 \\ imagenet-resnet-152-dag & “pool” (last fully-conn.) & None & ResNet-152-FC & 2048 \\ imagenet-resnet-152-dag & “res5c\_branch2c” (conv.) & BoVW & ResNet-152-BoVW & 2048 \\ imagenet-vgg-f & 20 (last fully-conn.) & None & VGG-F-FC & 4096 \\ imagenet-vgg-f & 13 (last conv.) & BoVW & VGG-F-BoVW & 4096 \\ imagenet-vgg-f & 13 (last conv.) & VLAD & VGG-F-VLAD & 4096 \\ imagenet-vgg-m & 20 (last fully-conn.) & None & VGG-M-FC & 4096 \\ imagenet-vgg-m & 13 (last conv.) & BoVW & VGG-M-BoVW & 4096 \\ imagenet-vgg-m & 13 (last conv.) & VLAD & VGG-M-VLAD & 4096 \\ imagenet-vgg-s & 20 (last fully-conn.) & None & VGG-S-FC & 4096 \\ imagenet-vgg-s & 13 (last conv.) & BoVW & VGG-S-BoWW & 4096 \\ imagenet-vgg-s & 13 (last conv.) & VLAD & VGG-S-VLAD & 4096 \\ imagenet-vgg-verydeep-16 & 36 (last fully-conn.) & None & VGG-VD-16-FC & 4096 \\ imagenet-vgg-verydeep-16 & 29 (last conv.) & BoVW & VGG-VD-16-BoVW & 4096 \\ imagenet-vgg-verydeep-16 & 29 (last conv.) & VLAD & VGG-VD-16-VLAD & 4096 \\ imagenet-vgg-verydeep-19 & 42 (last fully-conn.) & None & VGG-VD-19-FC & 4096 \\ imagenet-vgg-verydeep-19 & 35 (last conv.) & BoVW & VGG-VD-19-BoVW & 4096 \\ imagenet-vgg-verydeep-19 & 35 (last conv.) & VLAD & VGG-VD-19-VLAD & 4096 \\ vgg-face & 36 (last fully-conn.) & None & VGG-Face-FC & 4096 \\ vgg-face & 29 (last conv.) & BoVW & VGG-Face-BoVW & 4096 \\ vgg-face & 29 (last conv.) & VLAD & VGG-Face-VLAD & 4096 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary table of the off-the-shelf CNN-based features used in the experiments. * _Gradient-Based Local Binary Patterns_[69] (GLBP, 108 features)
* _Improved Local Binary Patterns_[70] (ILBP, 213 features)
* _Local Binary Patterns_[71] (LBP, 108 features)
* _Local Ternary Patterns_[72] (LTP, 216 features)
* _Texture Spectrum_[73] (TS, 2502 features)  
Each of the above methods was used to obtain a multiple resolution feature vector by concatenating the rotation-invariant vectors (e.g., LBP\({}^{ri}\)) computed at resolutions of 1 px, 2 px and 3 px [74].