## 5 Conclusions  
In this study, we proposed a method to overcome data deficiency and improve classification accuracy. Accordingly, we eliminated the background color and extracted the object while maintaining the original image ratio. In addition, new training images were generated via color perturbation based on the inverse PSNR with respect to color spaces and geometric transformations. Experiments were performed to demonstrate the reasonable combination by considering color spaces and geometric transformations and their combinations, respectively. First, we composed 11 training datasets to verify the validity of range determination for color perturbation: Traditional Method #1, Traditional Method #2, RGB PSNR, Lab PSNR, HSV PSNR, RGB PSNR + Lab PSNR, Lab PSNR + HSV PSNR, RGB PSNR + HSV PSNR, RGB-Lab PSNR, Lab-RGB PSNR. Traditional Methods #1 and #2 were related to adding the pixel values of the RGB color space arbitrarily. The other datasets were related to the proposed method. The experimental results showed that the classification accuracy could be improved by the proposed method as compared with the traditional methods, and the RGB PSNR + Lab PSNR training dataset showed better performance than the others. Second, we composed 21 training datasets to verify the effectiveness of geometric transformations: flipping with RGB PSNR, flipping with Lab PSNR, flipping with HSV PSNR, flipping with RGB PSNR \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Color Perturbation** & **Geometric** & **Number of** & **Classification** \\
**Experiment** & & **Transformation** & **Training Data** & **Accuracy** \\ \hline
1 & RGB PSNR & Rotation, Flipping & 414,720 & 92.0\% \\
2 & Lab PSNR & Rotation, Flipping & 460,800 & 92.0\% \\
3 & HSV PSNR & Rotation, Flipping & 460,800 & 92.0\% \\
4 & RGB PSNR + Lab PSNR & Rotation, Flipping & 875,520 & 95.0\% \\
5 & RGB-Lab PSNR & Rotation, Flipping & 4,147,200 & 93.0\% \\
6 & Lab-RGB PSNR & Rotation, Flipping & 4,147,200 & 94.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification accuracy obtained upon applying the inverse PSNR method and two geometric transformations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Color Perturbation** & \
\begin{tabular}{c} **Geometric** \\ **Transformation** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Training Data** \\ \end{tabular} & **Classification Accuracy (avg.)** \\ \hline RGB PSNR & Flipping & 10,000 & 76.0\%, 76.0\%, 75.0\% (75.7\%) \\ Lab PSNR & Flipping & 10,000 & 82.0\%, 79.0\%, 67.0\% (76.0\%) \\ HSV PSNR & Flipping & 10,000 & 70.0\%, 66.0\%, 64.0\% (66.7\%) \\ RGB PSNR + Lab PSNR & Flipping & 10,000 & 76.0\%, 71.0\%, 72.0\% (73.0\%) \\ RGB PSNR + Lab PSNR + HSV PSNR & Flipping & 10,000 & 80.0\%, 67.0\%, 78.0\% (75.0\%) \\ RGB-Lab PSNR & Flipping & 10,000 & 63.0\%, 75.0\%, 70.0\% (69.3\%) \\ Lab-RGB PSNR & Flipping & 10,000 & 71.0\%, 63.0\%, 69.0\% (67.7\%) \\ RGB PSNR & Rotation & 10,000 & 89.0\%, 89.0\%, 92.0\% (90.0\%) \\ Lab PSNR & Rotation & 10,000 & 87.0\%, 89.0\%, 89.0\% (88.3\%) \\ HSV PSNR & Rotation & 10,000 & 92.0\%, 89.0\%, 87.0\% (89.3\%) \\ RGB PSNR + Lab PSNR & Rotation & 10,000 & 92.0\%, 93.0\%, 90.0\% (91.7\%) \\ RGB PSNR + Lab PSNR + HSV PSNR & Rotation & 10,000 & 91.0\%, 81.0\%, 89.0\% (87.0\%) \\ RGB-Lab PSNR & Rotation & 10,000 & 93.0\%, 88.0\%, 89.0\% (90.0\%) \\ Lab-RGB PSNR & Rotation & 10,000 & 92.0\%, 90.0\%, 94.0\% (92.0\%) \\ RGB PSNR & Rotation, Flipping & 10,000 & 88.0\%, 90.0\%, 87.0\% (88.3\%) \\ Lab PSNR & Rotation, Flipping & 10,000 & 91.0\%, 89.0\%, 89.0\% (89.7\%) \\ HSV PSNR & Rotation, Flipping & 10,000 & 86.0\%, 91.0\%, 91.0\% (89.3\%) \\ RGB PSNR + Lab PSNR & Rotation, Flipping & 10,000 & 93.0\%, 87.0\%, 86.0\% (88.7\%) \\ RGB PSNR + Lab PSNR + HSV PSNR & Rotation, Flipping & 10,000 & 91.0\%, 88.0\%, 88.0\% (89.0\%) \\ RGB-Lab PSNR & Rotation, Flipping & 10,000 & 91.0\%, 90.0\%, 87.0\% (89.3\%) \\ Lab-RGB PSNR & Rotation, Flipping & 10,000 & 84.0\%, 89.0\%, 90.0\% (87.7\%) \\ \hline \hline \end{tabular}
\end{table}