# Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours
## 3 Results
### Features Based on the Dct and the Dft. a Comparative Study
#### 3.1.2 LOO-CV Tests
Table 1 shows, for the binary classification problem, a comparative summary of the network accuracy achieved for the best combinations of the DFT (three features) and the DCT (five features) depending on the hidden nodes (\(H\)), and Table 2 shows the same information but for the multi-class classification problem, for which the best combination of features is 5 in both the DCT and DFT feature configurations. Table 2 reveals that the best DFT-based configuration exceeds the best DCT-based one by almost two percentage points. In Table 1, however, it can be seen that the difference between using features derived from the DCT or the DFT is minimal. Regarding the case of the binary classification problem, no combination of features based on the DCT and the DFT was found that improved the results shown in Table 1. In the multi-class classification problem, the aggregation of the five features of each of the best options shown in Table 2 achieved an improvement in the results by almost one percentage point as shown in Table 3.  
Figure 10: The accuracy obtained by training SLFN with the DCT-based features. Figure 11 shows the confusion matrices and realizations for both the binary (a) and the multi-class classification (b) problems, respectively. The results were obtained by performing LOO-CV. In both sub-figures, a confusion matrix is shown with a resulting accuracy slightly above the corresponding mean accuracy value. From Figure 11b the correct classification rates of the different classes were acceptably high compared to the ones obtained for the class of _Lepidotrigla deiuzeidei_. As explained in this paper, the two points introduced by the specialist were used to segment the region of interest. The introduction of these points unambiguously determines the contour segment used to make the decision. However, this procedure can also be performed automatically. To show that, we conducted the following experiment. We took a set of 50 images, and we randomly took 40 of them to train a faster region-based convolutional network (Faster R-CNN) for object detection, leaving the rest of the images for the test. With this small amount of images, we were able to train a robust model that worked acceptably well. The procedure for training the model was as follows. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Binary-Class Classification Accuracy (LOO-CV 1000 Iterations)**} \\ \hline
**Hidden** & **DCT-5** & & & & **DFT-3** & & & \\
**Nodes** & **mean** & **std** & **max** & **min** & **mean** & **std** & **max** & **min** \\ \hline
7 & 88.44 & 0.031 & 95.65 & 78.26 & 88.46 & 0.032 & 95.65 & 76.09 \\
8 & 89.09 & 0.031 & 95.65 & 78.26 & 89.09 & 0.030 & 95.65 & 78.26 \\
9 & 89.37 & 0.030 & 97.83 & 80.44 & 89.29 & 0.031 & 95.65 & 76.09 \\
10 & 89.30 & 0.031 & 95.65 & 78.26 & **89.36** & **0.028** & **95.65** & **80.44** \\
11 & **89.34** & **0.029** & **95.65** & **80.44** & 89.19 & 0.030 & 95.65 & 78.26 \\
12 & 89.33 & 0.031 & 95.65 & 80.44 & 89.20 & 0.030 & 95.65 & 78.26 \\
13 & 89.04 & 0.031 & 95.65 & 78.26 & 89.10 & 0.030 & 95.65 & 80.44 \\
14 & 88.76 & 0.032 & 95.65 & 78.26 & 88.64 & 0.032 & 95.65 & 78.26 \\
15 & 88.80 & 0.031 & 95.65 & 78.26 & 88.57 & 0.031 & 95.65 & 76.09 \\
16 & 88.40 & 0.032 & 95.65 & 78.26 & 88.14 & 0.033 & 95.65 & 73.91 \\
17 & 88.05 & 0.033 & 95.65 & 78.26 & 87.92 & 0.033 & 95.65 & 76.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The binary classification problem. A comparative summary of the network accuracy obtained for the best combinations of the DCT (five features) and the DFT (three features) depending on the hidden nodes used. The LOO-CV was performed and the experiments were repeated 1000 times. For each feature set, there is an optimum number of hidden nodes that provide the best results. In the table the best accuracy results are highlighted in bold.