# Multiscale Analysis for Improving Texture Classification
## 5 Discussion  
For several reasons, combining multiple feature descriptors can effectively improve the performance and robustness of a texture classification or recognition system. One reason is that different feature descriptors can capture various aspects of the texture, such as frequency, orientation, contrast, and statistical dependencies between pixels. By using multiple feature descriptors, it is possible to capture a complete representation of an image's texture, which can improve the accuracy of the classification or recognition system. For example, the GLCM descriptor captures statistical dependencies between pairs of pixels in an image, which can help capture texture patterns such as texture boundaries and texture regions. The Haralick descriptor, on the other hand, captures statistical properties of an image, such as contrast and energy, which can help capture texture patterns such as texture density and texture directionality. The BiT descriptor captures the all-inclusive local and global patterns of a textural image. Finally, the Gaussian pyramid captures the spatial frequency content of an image at multiple scales, which can help capture texture patterns such as texture periodicity and texture regularity. Combining these feature descriptors makes it possible to capture a more comprehensive representation of an image's texture. As an additional benefit, combining numerous feature descriptors can make a texture classification or recognition system more robust and generalizable. By using multiple feature descriptors, the system can be more resistant to data variations, such as lighting or noise changes. This can be especially important when working with real-world data, where the conditions of the data may be difficult to control. For example, consider a texture classification system designed to recognize the texture of different fabrics. If an automatic system only uses a single feature descriptor, it may be sensitive to lighting variation or background noise, which could affect its performance. However, suppose the system uses multiple feature descriptors. In that case, it may be more robust to these variations, as each feature descriptor may capture different aspects of the texture that are less sensitive to them. Overall, combining multiple feature descriptors in this work is essential to effectively improve the performance and robustness of a texture classification or recognition system by capturing a more comprehensive representation of an image's texture and being more resistant to variations in the data. The proposed approach was assessed with three natural texture image datasets, the Outex, KTH-TIPS, and ALOT datasets, and two HI datasets, CRC, and BreakHis, with 24, 10, 250, 8, and 8 classes, respectively. The experiment protocol employed a train-test split (70/30) and 10-fold cross-validation. For the 70/30 experimental protocol on natural texture images, the results led to the following findings: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Magnification**} \\ \cline{3-6}  & & **40\(\times\)** & **100\(\times\)** & **200\(\times\)** & **400\(\times\)** \\ \hline [PERSON] et al. [40] & [PERSON] (LBP) & 75.60 & 73.00 & 72.90 & 71.20 \\ \hline [PERSON] et al. [40] & [PERSON] (GLCM) & 74.70 & 76.80 & 83.40 & 81.70 \\ \hline [PERSON] et al. [41] & [PERSON] (LBP) & 88.30 & 88.30 & 87.10 & 83.40 \\ \hline Ataky and [PERSON] [9] & [PERSON] (BiT) & 97.50 & 96.80 & 95.80 & 95.20 \\ \hline [PERSON] et al. [42] & CNN & 97.00 & 97.50 & 97.20 & 97.20 \\ \hline [PERSON] et al. [43] & CNN & 92.80 & 93.90 & 93.70 & 92.90 \\ \hline [PERSON] et al. [44] & CNN & 83.00 & 83.10 & 84.60 & 82.10 \\ \hline [PERSON] et al. [45] & CNN & 90.00 & 88.40 & 84.60 & 86.10 \\ \hline TiO+HistoB & Shallow & **98.64** & **97.85** & **98.76** & **98.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy (%) of shallow and deep approaches on the BreakHis dataset. All works used the same data partitions for training and testing.