# Multiscale Analysis for Improving Texture Classification
## 4 Experimental Results
### Experiments with Texture Datasets  
There have been numerous important advances in texture classification in the literature during the past few decades. Many of them, however, are new takes on old ideas. Choosing which examples best illustrate the state of the art is difficult. Therefore, we focused on the first ground-breaking initiatives in texture classification. The experimental protocol to assess the proposed approach's validity included comparisons to important shallow and deep-based methods. Figure 3: Sample images from different datasets. Tables 1 and 2 present the results of the proposed method (TiO), BiT, GLCM, and Haralick descriptors on the Outex, KTH-TIPS, and ALOT datasets, using the train-test split and 10-fold cross-validation, respectively. For each dataset, we present the results obtained by combining all the descriptors (TiO) and the results obtained with each descriptor individually. The main purpose was to verify the effectiveness of TiO and the descriptors' complementary. The proposed method achieved the best average accuracy of 100% on the Outex dataset with the LDA and CatBoost classifiers. On the ALOT dataset, the proposed method achieved an average accuracy of 98.48% with the LDA classifier. Again, TiO outperformed all individual descriptors. Finally, on the KTH-TIPS dataset, the proposed method achieved the best average accuracy of 100% with the LDA classifier. Moreover, TiO outperformed all individual descriptors. Despite the different experimental protocols (train-test split and 10-fold cross-validation), the results were similar for nearly all the classifiers on the respective datasets. Different works have used the Outex dataset for texture classification. For instance, [PERSON] and [PERSON] [25] introduced an approach based on a rotation-invariant LBP, achieving an accuracy of 96.26% with a \(k\)-NN classifier. [PERSON] et al. [26] presented a rotation-invariant, impulse-noise-resistant, and illumination-invariant approach based on a local \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Texture Descriptors**} & \multicolumn{5}{c}{**Classification Algorithms**} \\ \cline{3-6}  & & **HistoB 1** & **Light B2** & **LDA** & **CatBoost 3** \\ \hline \multirow{4}{*}{Outex} & TiO & **100.0** & **100.0** & **100.0** & **100.0** \\  & BiT & 99.92 & 99.69 & **100.0** & 99.84 \\  & GLCM & 98.69 & 97.99 & 95.91 & 99.30 \\  & Haralick & 98.84 & 99.53 & 99.38 & 99.84 \\ \hline \multirow{4}{*}{ALOT} & TiO & 71.50 & 73.07 & **98.48** & 93.47 \\  & BiT & 65.71 & 66.52 & 78.14 & 74.90 \\  & GLCM & 51.22 & 54.32 & 66.61 & 64.23 \\  & Haralick & 68.52 & 63.41 & 84.57 & 83.23 \\ \hline \multirow{4}{*}{KTH-TIPS} & TiO & 98.53 & 97.20 & **100.0** & 98.91 \\  & BiT & 96.29 & 96.70 & 99.58 & 97.53 \\ \cline{1-1}  & GLCM & 89.71 & 90.12 & 87.65 & 92.01 \\ \cline{1-1}  & Haralick & 95.88 & 96.29 & 98.35 & 96.11 \\ \hline \hline \end{tabular}  
* max\_bins = 10, max\_iter = 1500. 23 n\_estimators = 1500.  
\end{table} 
Table 1: Average accuracy (%) on the test set of the Outex, ALOT, and KTH-TIPS datasets. The best accuracy for each dataset is shown in boldface.