## 5 Ablation Study and Discussions
### Impact of Different Edge/Boundary Detection Methods  
In the proposed method, a Canny edge detector [40] is chosen to provide binary edge results for filter selection; see the pink box in Figure 2. The use of Canny edge detection is motivated by its ability to extract object boundaries and visually salient edges that preserve the essential structure of the image while ignoring weakly related information [38; 39].  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Image Type**} & \multicolumn{2}{c}{**AlexNet\({}^{\star}\)**} & \multicolumn{2}{c}{**AlexNet**} & \multicolumn{2}{c}{**VGG16**} & \multicolumn{2}{c}{**ResNet50**} \\ \cline{2-9}  & **Acc. (\%)** & **Time (s)** & **Acc. (\%)** & **Time (s)** & **Acc. (\%)** & **Time (s)** & **Acc. (\%)** & **Time (s)** \\ \hline Computer-generated & 90.5 & 731.4 & **84.4** & **46.3** & 88.0 & 455.4 & 86.0 & 232.2 \\ Scans of & 62.1 & 363.3 & **65.8** & **39.4** & 65.3 & 96.3 & 65.5 & 72.1 \\ actual fabrics & All images & 64.3 & 366.8 & **67.3** & **40.0** & 66.4 & 113.5 & 67.5 & 85.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparisons of using a different number of filters and pre-trained CNNs.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**AlexNet\({}^{\star}\)**} & \multicolumn{2}{c}{**AlexNet**} & \multicolumn{2}{c}{**VGG16**} & \multicolumn{2}{c}{**ResNet50**} \\ \cline{2-9}
**Layers** & **num\_filter** & **size\_fmap** & **num\_filter** & **size\_fmap** & **num\_filter** & **size\_fmap** & **num\_filter** & **size\_fmap** \\ \hline conv1 & 96 & \(55\times 55\) & 1 & \(55\times 55\) & 1 & \(224\times 224\) & 1 & \(112\times 112\) \\ conv2 & 256 & \(27\times 27\) & 1 & \(27\times 27\) & 1 & \(112\times 112\) & 1 & \(56\times 56\) \\ conv3 & 384 & \(13\times 13\) & 1 & \(13\times 13\) & 1 & \(56\times 56\) & 1 & \(28\times 28\) \\ conv4 & 384 & \(13\times 13\) & 1 & \(13\times 13\) & 1 & \(28\times 28\) & 1 & \(14\times 14\) \\ conv5 & 256 & \(13\times 13\) & 1 & \(13\times 13\) & 1 & \(14\times 14\) & 1 & \(7\times 7\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detailed setup of the extended experiments.  
Additionally, the binary nature of the Canny edge output makes it computationally efficient and straightforward to combine with feature maps. Specifically, the binary edge map ([0,1]) can be directly multiplied with feature maps, enabling the extraction of more relevant and effective information. Experiments were conducted to compare the performance of Canny with a learning-based edge detection method, BDCN [39], for filter selection and feature extraction. BDCN achieves superior performance in edge detection [39], as shown in Table 7, the overall accuracy of our method remained identical (67.3%) when using either Canny or BDCN. However, using BDCN significantly increased the computational time from 40.0 s (of Canny) to 44.7 s (of BDCN), making it less efficient for practical application. This is particularly evident for computer-generated images, where the runtime increased by over 40 s. These findings demonstrate that while BDCN achieved superior edge detection performance in isolation, it did not improve the accuracy of our method and added unnecessary computational overhead. Canny is, therefore, chosen in our method, considering both accuracy and efficiency.