## 4 Experimental Results and Discussion
This was because the number of training images was critical for improving the classification accuracy. In addition, in this study, the classification result of combining RGB PSNR + Lab PSNR showed better performance when two training datasets were combined, as presented in Table 3. Therefore, we performed additional experiments regarding overlapping the inverse PSNR method on two color spaces: the first one (RGB-Lab PSNR) was applying Lab PSNR after applying RGB PSNR; the other one (Lab-RGB PSNR) was applying RGB PSNR after applying Lab PSNR. The reason behind considering the processing order was to ascertain whether the processing order was influential or not. The experimental results are presented in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Training Images** & **Classification Accuracy** \\ \hline Traditional Method \#1 & 2880 & 47.0\% \\ Traditional Method \#2 & 2880 & 34.0\% \\ RGB PSNR & 2880 & 50.0\% \\ Lab PSNR & 3200 & 63.0\% \\ HSV PSNR & 3200 & 53.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracies of traditional methods and methods that use the inverse PSNR. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Training Images** & **Classification Accuracy** \\ \hline RGB PSNR + Lab PSNR & 6080 & 67.0\% \\ Lab PSNR + HSV PSNR & 6400 & 66.0\% \\ RGB PSNR + HSV PSNR & 6080 & 64.0\% \\ RGB PSNR + Lab PSNR + HSV PSNR & 9280 & 54.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the classification accuracy by overlapping the inverse PSNR method about two color spaces. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Training Images** & **Classification Accuracy** \\ \hline Traditional Method \#1 & 2880 & 44.0\% \\ Traditional Method \#2 & 2880 & 43.0\% \\ RGB PSNR & 2880 & 62.0\% \\ Lab PSNR & 2880 & 62.0\% \\ HSV PSNR & 2880 & 63.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of traditional methods and the methods that use the inverse PSNR, with an equal number of training images. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Training Images** & **Classification Accuracy** \\ \hline RGB PSNR + Lab PSNR & 6080 & 67.0\% \\ Lab PSNR + HSV PSNR & 6400 & 66.0\% \\ RGB PSNR + HSV PSNR & 6080 & 64.0\% \\ RGB PSNR + Lab PSNR + HSV PSNR & 9280 & 54.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the classification accuracy by joining the PSNR results. Comparing Tables 1 and 4, the result obtained upon overlapping the inverse PSNR method on two color spaces was better than that obtained by adding up the three training datasets generated using the inverse PSNR method in terms of each color space. Therefore, we performed the experiment considering the following three categories: the case of the inverse PSNR method on only one color space, the case of adding up the training datasets, and the case of overlapping the inverse PSNR method on two color spaces. As previously mentioned, two geometric transformations, namely flipping and rotation, were applied to each training dataset performed using the inverse PSNR method, as the target experiment was related to the conveyor. First, we applied the flipping method after applying the color perturbation based on the inverse PSNR method. The results are presented in Table 5.  
The experimental results showed that Experiment #4 yielded better performance than the other experiments. The training data were composed by adding the two training datasets generated using the inverse PSNR method with respect to the RGB color space and CIE L*a*b* color space, respectively, and then performing the flipping method (horizontal, vertical, and horizontal-vertical). Although Experiments #5, #6, #7 had more training data than that of Experiment #4, they showed lower performance regarding test data. This meant that the number of training datasets was not the only reason for the improvements in the classification results.