## 4 Comparative Analysis of RPLC with CNN through Experimental Validation
### Benchmarking Analysis
The reason of such altering results is absence of coherence between the input features. CNN seems unable to decide the correct class labels for low resolution images (refer 3 rd and 4 th row in Table 4). For scenario Id 7, the Dark images, as annotated by an expert, are misclassified to be Semi  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario ID** & **Correctly Classified** & **Incorrectly Classified** \\ \hline \(1\) & \\ (Basic CNN—Image size 100 by 100, Dataset size 300) & \\ (Basic CNN—Image size 640 by 640, Dataset size 900) & \\ (  
dark images, whereas the same images are classified correctly if the network is trained over high resolution images with the same image labels. This uncovers the fact that with increased resolution of images along with higher training times, CNN performs differently as opposed to the network trained on the low resolution images. Table 5 shows the classification results of RPLC for the similar images as reported by CNN. The tests are performed over the actual image sizes provided in image dataset without any preprocessing. Row 1, 2, 3 shows the classification results of bright images along with the misclassified results for Dark, Semi-dark, and Bright images, as interpreted by a human annotator. To further validate the results, we take the confusion matrices of the classification generated by CNN and luminance based algorithm for the test case focusing 300 image datasets for the analysis of percentages for correct and incorrect classification rate. The respective confusion matrices in percentage form for total count of images in each case are shown in Figure 10. \begin{table}
\begin{tabular}{c c c} \hline
**Specification of** & & \\
**Luminance-Based** & **Row ID** & **Correctly Classified** & **Incorrectly Classified** \\
**Algorithm** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of RPLC. correctly classified images is up to 72% whereas incorrectly classified images are approximately around 27.5%. For Figure 10d the percentages for correct and incorrect classification are 73% and 26.6%, respectively. For Figure 10e,f the correctly classified percentage count is up to 77% and 78% and the percentage count for incorrectly classified images is 23% and 22%. Here again RPLC algorithm retains its consistency for the image classification. One of the aspects of presented analysis worth mentioning is that the confusion matrices differ in number of total number of samples used. As the requirements imposed by the CNN includes the division of dataset into small subsets of training, testing and validation the correct and incorrect accuracies as mentioned in Figure 10a,b represents only the validation set whose sizes is 25 images only. Similarly, the confusion matrices for SVM presented in Figure 10c,d represents only the stats for testing set which is 40% of the total image dataset size. This implies there are higher possibilities of obtaining incorrect results for larger datasets whereas the confusion matrix shown for luminance-based algorithm has a total of 300 images which is the total amount of images used for that scenario. For the discourse of gains achieved relating to the luminance-based algorithm over the CNN based approach for the classification of images, further discussion on the numeric analysis is presented in Tables 2 and 3. For the gain calculation presented in Table 6, difference of accuracies among luminance-based and CNN based classification is calculated, after which the difference is divided by the accuracy of CNN based classification accuracy to generate gain of RPLC algorithm over the CNN based classification. For the comparison of Scenario Id 5 \(v/s\) 7, the accuracy gains are observed to reach approximately 89.25% for the training set and 8.14% for the testing set. Whereas, even for worst case RPLC algorithm still achieves 0.13% better performance for the validation set as compared to CNN based algorithm. Following the same calculation pattern for gain identification.