# Deep Feature Retention Module Network for Texture Classification
## 4 Experiment
### Experiment Method and Result  
The output values of each DenseBlock were concatenated into four combinations to analyze the impact of multiple levels in various settings, as shown in Figure 6. As shown in Figure 6a, the output values of each DenseBlock are concatenated, and the feature information of all levels is used for learning. Subsequently, from Figure 6b-d, the structure of the model is constructed and learned by sequentially excluding the DenseBlock. Figure 6d shows a structure in which only high-level features were used. Additionally, each method commonly included the last DenseBlock, which outputted high-level features that significantly influenced classification performance. The learning results of the model learned in the four combinations are listed in Table 1, and the four combinations are sequentially listed in Table 1 as Blocks 14, 24, 34, and 4. The results from Blocks 14 to 4 showed that the structure of Block 14, used by combining all levels of output values, was more effective for learning than Block 4, which used only high-level output values. In other words, when using the proposed structure with the FRM, it was confirmed that the datasets with regularity and irregularity in the image were effectively classified. In addition, the proposed method on the KTH-TIPS2b dataset with regularity within classes was effective in terms of performance. The analysis of the experimental results for each dataset is as follows: In the DTD results, the proposed methods showed higher accuracy than the results of the previously proposed methods. In particular, in the case of Block 14, using all the feature information from low to high levels, the highest result was 74.53%. This is a +4.93% increase compared to BP-CNN (69.6%) and a +0.18% increase compared to Block 4, which used only high-level features. It was identified that images with irregular shapes between classes were efficiently classified when different features were used together rather than using only high levels. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Backbone** & **DTD** & **FMD** & **KTH-TIP2b** \\ \hline BP-CNN [30] & VGG19 & 69.6 & 77.8 & 75.1 \\ FV-CNN [31] & VGG19 & 72.3 & 79.8 & 75.4 \\ LFV [32] & VGG19 & 73.8 & 82.1 & 82.6 \\ FASON [17] & VGG19 & 72.9 & 82.1 & 76.5 \\ DeepTEN [33] & ResNet50 & 69.6 & 80.2 & 82.0 \\ LSCNet [34] & VGG16 & 71.1 & 82.4 & 76.9 \\ CLASSNet [4] & ResNet18 & 71.5 & 82.5 & 85.4 \\ CLASSNet [4] & ResNet50 & 74.0 & **86.2** & 87.7 \\ \hline Block 14 (Ours) & DenseNet161 & **74.53** & 82.76 & **92.97** \\ Block 24 (Ours) & DenseNet161 & 74.52 & 82.07 & 91.55 \\ Block 34 (Ours) & DenseNet161 & 74.35 & 81.72 & 91.83 \\ Block 4 (Ours) & DenseNet161 & 74.35 & 81.72 & 91.76 \\ \hline \end{tabular}
\end{table} 
Table 1: Evaluation results on texture datasets. Bold denotes best accuracy. The results of the FMD showed that the accuracy of Block 14 using different levels of features was higher than that of the previous cases, except for CLASSNet. In addition, among the proposed methods, Block 14 showed an increase of +1.04% compared to Block 4 (81.72%). Similar to the results of DTD, the combination of different levels of features when classifying images with irregular forms is effective at extracting detailed information within the image. In the dataset KTH-TIPS2b, the accuracy of Block 14 was higher than the others. Compared with the results of Block 4, Block 14 showed better accuracy. The results proved that, similar to other datasets, it is advantageous to use various levels rather than only high levels to analyze datasets. However, exceptionally, the result of Block 24 showed some performance reduction. Block 24 has a structure that does not additionally combine features for DenseBlock 1. In Table 1, the performance of Block 24 is slightly lower, but Block 14 combined with all blocks improves performance again. If there are many holes in the bread, such as in the case of white bread in Figure 5c, more detailed information is needed to distinguish bread.