# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metric/Operator**} & \multirow{2}{*}{\begin{tabular}{c} **Average Accuracy** \\ **and Associated** \\ **Standard Deviation** \\ \end{tabular} } & \
\begin{tabular}{c} **Macro-Averaging** \\ **Precision and** \\ **Associated Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Macro-Averaging** \\ **Recall and** \\ **Associated Standard** \\ **Deviation** \\ \end{tabular} \\ \hline
**3D GLCM [11]** & 93.67 \(\pm\) 0.57 & 94.04 \(\pm\) 0.51 & 93.67 \(\pm\) 0.57 \\
**IGLCM_3D [29]** & 95.44 \(\pm\) 0.43 & 95.84 \(\pm\) 0.4 & 95.44 \(\pm\) 0.44 \\
**LBP_2D [8]** & 88.84 \(\pm\) 1.03 & 89.9 \(\pm\) 0.89 & 88.84 \(\pm\) 1.03 \\
**BM3 DELBP_3D [28]** & 96.86 \(\pm\) 0.47 & 97.12 \(\pm\) 0.42 & 96.86 \(\pm\) 0.47 \\
**Typical CNN** & 75.04 \(\pm\) 0.62 & 79.27 \(\pm\) 1.02 & 75.08 \(\pm\) 0.6 \\
**Proposed method** & **98.34 \(\pm\) 0.36** & **98.51 \(\pm\) 0.31** & **98.34 \(\pm\) 0.36** \\ \hline \hline \end{tabular} The results are averaged over 100 random partitions of the train and test sets.  
\end{table} 
Table 7: Classification results for the second experiment with the SVM classifier [%]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metric/Operator**} & \multirow{2}{*}{\begin{tabular}{c} **Average Accuracy** \\ **and Associated** \\ **Standard Deviation** \\ \end{tabular} } & \
\begin{tabular}{c} **Macro-Averaging** \\ **Precision and** \\ **Associated Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Macro-Averaging** \\ **Recall and** \\ **Associated Standard** \\ **Deviation** \\ \end{tabular} \\ \hline
**3D GLCM [11]** & 86.61 \(\pm\) 0.83 & 86.94 \(\pm\) 0.96 & 86.61 \(\pm\) 0.83 \\
**IGLCM_3D [29]** & 91.61 \(\pm\) 0.78 & 92.18 \(\pm\) 0.72 & 91.60 \(\pm\) 0.78 \\
**LBP_2D [8]** & 82.63 \(\pm\) 1.16 & 84.42 \(\pm\) 1.16 & 82.63 \(\pm\) 1.16 \\
**BM3 DELBP_3D [28]** & 91.57 \(\pm\) 0.72 & 92.41 \(\pm\) 0.72 & 91.56 \(\pm\) 0.72 \\
**Proposed method** & **93.98 \(\pm\) 0.61** & **94.59 \(\pm\) 0.60** & **93.97 \(\pm\) 0.61** \\ \hline \hline \end{tabular} The results are averaged over 100 random partitions of the train and test sets.  
\end{table} 
Table 8: Classification results for the second experiment with the kNN classifier [%]. Figure 15: (**a**) smoothed misclassified sample from class PerlinNoise (**b**) smoothed training image from class PerlinNoise (**c**) smoothed training image from class Uwari. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metric/Operator**} & \multirow{2}{*}{\begin{tabular}{c} **Average Accuracy** \\ **and Associated** \\ **Standard Deviation** \\ \end{tabular} } & \
\begin{tabular}{c} **Macro-Averaging** \\ **Precision and** \\ **Associated Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Macro-Averaging** \\ **Recall and** \\ **Associated Standard** \\ **Deviation** \\ \end{tabular} \\ \hline
**3D GLCM [11]** & 86.61 \(\pm\) 0.83 & 86.94 \(\pm\) 0.96 & 86.61 \(\pm\) 0.83 \\
**IGLCM_3D [29]** & 91.61 \(\pm\) 0.78 & 92.18 \(\pm\) 0.72 & 91.60 \(\pm\) 0.78 \\
**LBP_2D [8]** & 82.63 \(\pm\) 1.16 & 84.42 \(\pm\) 1.16 & 82.63 \(\pm\) 1.16 \\
**BM3 DELBP_3D [28]** & 91.57 \(\pm\) 0.72 & 92.41 \(\pm\) 0.72 & 91.56 \(\pm\) 0.72 \\
**Proposed method** & **93.98 \(\pm\) 0.61** & **94.59 \(\pm\) 0.60** & **93.97 \(\pm\) 0.61** \\ \hline \hline \end{tabular} The results are averaged over 100 random partitions of the train and test sets.  
\end{table} 
Table 9: Classification results for the second experiment with the RF classifier [%]. As in the first experiment, SVM proves to be the most performant classifier. The proposed approach achieves a promising performance even for a high number of image categories and a relatively small number of samples, providing the best classification results from all the considered techniques. The CNN-based method is not able to generalize very well, to adapt to new test images for such a small number of images per class, while handcrafted features prove to work well even in this situation.