# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 4 Experiments and Results
### Comparison with Other Approaches  
The values presented above correspond to fitness after the evolutionary cycle of BP to validate our work. The benchmark offers two modalities: one which uses only 60% of the database and another containing all images; we keep the first option. Table 19 shows the final results achieved on the testing set over ten random splits with our best program considering the FT dataset. Here, we appreciate the final results considering the following algorithms for salient object detection: FT--frequency-tuned, GC--global contrast, SF--saliency filters, PCAS--principal component analysis, and DHSNet. Additionally, we include the original proposal of the artificial dorsal stream named focus of attention (FOA) reported in [5]. Note that we overpass all other algorithms in the benchmark. FT is the most straightforward dataset since all objects are in focus and cover a significant portion of the image. We remark on the poor performance of DHSNet, which contrasts with the results reported in [31]. This poor performance may be because validation images are not part of the training. Figure 3 provides precision-recall curves for the benchmark FT, IMGSAL, and PASCAL-S databases. Again, our methods score highest in all datasets. However, GVBSBP + MCG scores highest in the FT and PASCAL-S datasets, while GBVSBP achieves better results in IMGSAL. The rough segmentation in this last dataset worsens the segmentation process. Figure 4 presents image results of all algorithms in the three databases for visual comparison. According to the results of Figure 3 our methods achieve outstanding results; however, we provide the following images to spot some challenges in the benchmark. The rows provide the best method (leftmost) and worst method (rightmost), starting with the original and finalizing with the ground truth. The idea of selecting these images highlights that we cannot blindly follow the statistical results without observing the outcomes. As we mentioned, FT is the most straightforward test. The mailbox is the object of interest, and we can agree that our proposed method, including the segmentation step, is slightly better than the others, but not without this crucial element. The following image provides an example of a questionable ground truth (the horse), which is not the most conspicuous object. None of the algorithms correctly identify the object. The last image provides an example of ground truth that we do not consider imprecise, but because the evaluation rests on the average, the ranking, in this case, is controversial. Note that even if the computer model is symbolic, the interpretation remains numeric and therefore, there exist computer errors. Nevertheless, the computation is data independent since the proposal follows a function-driven paradigm. Figure 5 illustrates the image processing through the whole GBVSBP+MCG program. The programs inserted in the template and provided in Table 18 are quite simple, helping in the overall explanation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Saliency Model** & **Score (F-Measure)** \\ \hline GBVSBP + MCG & 86.72 \\ SF [41] & 85.38 \\ GBVS + MCG [30] & 85.33 \\ PCAS [42] & 83.93 \\ GC [43] & 80.64 \\ DHSNet [6] & 74.06 \\ FT [44] & 71.23 \\ GBVSBP & 69.08 \\ GBVS [30] & 65.25 \\ FOA [5] & 60.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Comparison using the benchmark with other algorithms in the FT database [30]. This ranking of solutions applies a set of images separated from the rest of the photos as a challenging test. The evolved solution improves the original proposal and defeats DHSNet by a large margin. Figure 3: The precision–recall curve for FT, PASCAL-S, and IMGSAL datasets using the following algorithms: GBVSBP + MCG, SF, GBVS + MCG, PCAS, GC, FT, GBVSBP, and GBVS. Next, we test our best solution (GBVSBP + MCG) on four databases studied in [6], and the results are in Table 20. [PERSON] et al. designed the best solution, trained it with MSRA-A, then tested it with MSRA-BTest, ECSSD, SED2, and iCoseg.