## 3 Results  
### Results and Predictions on Test Pictures  
In order to compare the results between the different models generated, an accuracy indicator was defined as the percentage of pixels correctly classified over the total of the image:  
\[\text{tp}=\text{true positive; }\text{tn}=\text{true negative; }\text{fp}=\text{false positive; }\text{fn}=\text{false negative}\]  
\[\text{Accuracy}=\frac{\text{tp}+\text{tn}}{\text{tp}+\text{tn}+\text{fp}+ \text{fn}} \tag{1}\]  
The evaluation is made not only between the best models obtained for SVM and deep-learning, but also between several models generated with different parameters in both cases. For the SVM model, the results were obtained with different dataset sizes: 50, 122, 190 and 251 training images. SVM test  
Figure 3: Mask manual edition example. (**a**) original picture; (**b**) clustering classification mask; (**c**) edited mask. accuracy results are presented in Table 1. For every size, the mean test accuracy of all 50 models iteratively generated with different dataset distributions was calculated, although only the mean test accuracy of the best model is presented in Table 1. Model number 4, which is the one that was trained with a larger dataset, was found to be the one with the best average accuracy (83.09%) and the best average accuracy for all the iterations of generation of the model (82.53%). Moreover, model 4 was determined as the best model, with the highest accuracy in 64.29% of the test pictures. The best model indicator was defined as the percentage of test pictures that present the best result with each model. This percentage was also reached considering 1% better test accuracy results for the model 4. Quantifying this evolution of percentages reveals small differences between model 4 and the rest in the cases it is not the best, presenting a higher accuracy or up to 1% lower in 89.29% of the test pictures. As expected, the accuracy of the model grows as the number of training examples increases. Focusing on the best SVM model obtained, whose learning curve is shown in Figure 4, a case of high bias is observed, which may indicate an underfitting problem. The model does not fit the data sufficiently, it lacks information and requires more parameters to reduce the error. Specifically, method limitations were observed with regard to the clustering and SVM structure. Clustering for data preparation and SVM training are strictly conducted by the colour space parameters of the pixels, which seem to be insufficient for the segmentation. The problem does not apparently be related to the selection of channels or colour spaces, neither with the absence to add one of them, but with the nature of the method. Additional procedures are required for a segmentation that performs an analysis based on regions and textures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SVM** & **1** & **2** & **3** & **4** \\ \hline Images & 50 & 122 & 190 & 251 \\ Mean Test Accuracy of the best model (\%) & 74.90 & 77.95 & 82.48 & 83.09 \\ Best model (\%) 1 & 3.57 & 10.71 & 21.43 & 64.29 \\ Best model 4 @1\% (\%) 2 & 3.57 & 7.14 & 0.00 & 89.29 \\ Mean Test Accuracy of Model Generation Iterations (\%) & 73.71 & 76.59 & 81.20 & 82.53 \\ \hline \hline \end{tabular}  
* Percentage of the pictures that present the best result with each model. 2 Best model considering 1% better the model number 4.  
\end{table} 
Table 1: Support vector machine (SVM) test accuracy results. Figure 4: SVM 4 learning curve. Once the best model was chosen, training was executed employing the same parameters with different dataset sizes in order to analyse the evolution of the test accuracy. The aim was to predict the possibility of model improvement in case we were to add new training pictures. The dataset size varied between 20 and 121 original images, i.e., 160 and 968 images after applying data augmentation, with 10 original images steps.