# Multiscale Analysis for Improving Texture Classification
## 4 Experimental Results
### Experiments with Texture Datasets
\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 & 99.93 (0.001) & 99.88 (0.002) & **100.0 (0.0)** & 99.49 (0.003) \\  & BiT & 99.81 (0.001) & 99.53 (0.003) & 99.69 (0.003) & 99.88 (0.001) \\  & GLCM & 97.16 (0.003) & 98.24 (0.004) & 96.52 (0.006) & 98.93 (0.004) \\  & Haralick & 99.18 (0.001) & 99.19 (0.002) & 99.64 (0.003) & 99.29 (0.001) \\ \hline \multirow{4}{*}{ALOT} & TiO & 42.82 (0.009) & 53.31 (0.012) & **97.89 (0.015)** & 95.89 (0.022) \\  & BiT & 29.81 (0.001) & 32.53 (0.003) & 83.65 (0.002) & 82.12 (0.013) \\ \cline{1-1}  & GLCM & 13.14 (0.015) & 35.12 (0.212) & 69.72 (0.023) & 72.62 (0.010) \\ \cline{1-1}  & Haralick & 18.45 (0.020) & 29.57 (0.002) & 86.54 (0.024) & 81.33 (0.021) \\ \hline \multirow{4}{*}{KTH-TIPS} & TiO & 98.03 (0.013) & 97.41 (0.027) & **99.98 (0.006)** & 98.66 (0.018) \\ \cline{1-1}  & BiT & 96.61 (0.023) & 96.80 (0.026) & 97.88 (0.006) & 97.79 (0.023) \\ \cline{1-1}  & GLCM & 90.86 (0.030) & 90.74 (0.035) & 84.44 (0.014) & 90.12 (0.034) \\ \cline{1-1}  & Haralick & 95.29 (0.017) & 96.04 (0.023) & 96.75 (0.004) & 96.40 (0.011) \\ \hline \hline \end{tabular}  
* max\_bins = 10, max\_iter = 1500. 23 n\_estimators = 1500.  
\end{table} 
Table 2: Average accuracy and standard deviation (%) on the Outex, ALOT, and KTH-TIPS datasets using a 10-fold cross-validation setup. The best accuracy for each dataset is shown in boldface. spiking pattern (LSP). This approach achieved an accuracy of 86.12% with a neural network but was not extended to color textures and required several input parameters. [PERSON] and [PERSON] [9] introduced a bioinspired texture descriptor based on biodiversity (species richness and evenness) and taxonomic measures. The latter represented the image as an abstract model of an ecosystem where species' diversity, richness, and taxonomic distinctiveness measures were extracted. Such a texture descriptor was invariant to rotation, translation, and scale and achieved an accuracy of 99.88% with an SVM. Table 3 compares our best results with a few works that also used the Outex dataset. Table 4 presents a few works that also used the ALOT dataset. There was an improvement in the accuracy of nearly 1% and 0.1% compared with shallow and deep methods, respectively. Notwithstanding the difference is not significantly high, it is worth mentioning that the success of CNNs relies on the ability to leverage massive, labeled datasets to learn high-quality representations. Nonetheless, data availability for a few domains may be restricted, so CNNs become restrained from several fields. Moreover, some works evaluated small-scale CNN architectures, such as T-CNN and T-CNN Inception, with 11,900 and 1.5M parameters, respectively. Despite the reduced number of parameters and lower computational cost for training, both still required a large quantity of training data to perform satisfactorily. Even some small architectures of comparable performance, such as MobileNets, EfficientNets, and sparse architectures resulting from pruning, still have many training parameters. For instance, the number of parameters of a MobileNet CNN ranges between 3.5M and 4.2M, while the number of parameters of EfficientNet CNNs range between 5.3M and 66.6M. GoogleNet, ResNet, and VGG CNNs generally need extensive training, and the number of hyperparameters and the computational cost is high.