## 4 Experimental Results and Discussion  
In this study, we proposed a method to improve the image classification accuracy for small datasets. We applied transfer learning because a method that updates the weights of fully connected layers is more effective than one that updates the weights of all the layers of the network [34]. The aim of the experiment was to classify the objects of 10 categories. Identifying images from these categories is difficult because they are uncommon and not general items, whereas general items are contained in well-known and existing image datasets. Therefore, the training images should be collected first, although significantly few images were manually collected as the training data. The original images were acquired as the training data using the vision sensor. The number of original images was 32 in each category. In addition, the test data were collected in the same manner as before. The test data were not contained in the training dataset. All experimental results were verified with test data, which was not generated arbitrarily and just captured from the vision sensor. Classification accuracy in the tables indicates the results classifying the object of test data. To verify the proposed method, first we compared the traditional method with the proposed one. Especially, to demonstrate the color perturbation with respect to the characteristic of color spaces and the geometric transformations, we compared and analyzed the experiments of each color space and each geometric transformation. Table 1 presents the results of image classification by comparing the traditional methods with the methods that apply color perturbation based on the inverse PSNR with respect to three color spaces based on the proposed method. The traditional methods work by adding random values in the color channels. Traditional Methods #1 and #2 arbitrarily transform the pixel values in terms of the RGB color space. Traditional Method #1 adds positive values, whereas Traditional Method #2 adds positive or negative values. As presented in this table, the case of adding only positive values had better performance compared with the case of adding positive or negative values. RGB PSNR indicates the generated training data by applying color perturbation based on the inverse PSNR with respect to the RGB color space. Lab PSNR represents the generated training data by applying color perturbation based on the inverse PSNR with respect to CIE L*a*b* color space. HSV PSNR denotes the generated training data by applying color perturbation based on the inverse PSNR with respect to HSV color space. In addition, the methods that apply color perturbation based on the inverse PSNR had better classification accuracy than that of the traditional methods. To conduct the experiments in the same condition, we arbitrarily extracted an equal number of training images with respect to the methods listed in Table 1. The classification results are presented in Table 2.  
In addition, Table 2 shows that the methods that applied color perturbation based on inverse PSNR could improve the classification accuracy better compared with the traditional methods. The combination of extracted images was reflected in the classification results. Next, another experiment was performed to compare the cases of joining the training data generated via the inverse PSNR together with respect to each color space. The results are presented in Table 3.  
From Table 3, it was evident that increasing the number of training images did not always guarantee an improvement to the classifier performance. This was because the classification result obtained upon joining the training data via inverse PSNR in terms of RGB, Lab, and HSV color spaces was 54.0%, while the classification results obtained upon joining two training datasets among the dataset generated via the inverse PSNR in terms of RGB, Lab, and HSV color space had higher performance than 54.0%. In a previously conducted research, we generated the training dataset by applying Lab PSNR after applying RGB PSNR [40].