# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 4 Experiments and Results
### Comparison with Other Approaches
We observe that we scored highest on three datasets, MSRA-BTest, ECSSD, and iCoseg, while achieving competitive results on SED2. We provide such a comparison since [6] did not test their algorithms with the benchmark protocol. Therefore it is hard to make a clear comparison between both approaches, and the results we provide here illustrate the methodologies' performance. We provide a final set of experiments in Table 21 to illustrate the behavior of our evolutionary proposal in comparison with deep learning methodologies. Since such state-of-the-art methodologies run with small images, we run all experiments with images of \(224\times 224\) pixels, including our evolved GBVS, which does not have such limitations. As shown in Figure 4, GBVSBP + MCG is the best algorithm in our test against classic algorithms for FT and PASCAL-S, while GBVSBP is the best for IMGSAL. The results shown in these new experiments with IMGSAL exhibit its good performance against DHSNet and tying BASNet with a lower variance. Regarding FT, we observe that GBVSBP achieves the lowest score of all learning approaches. Nevertheless, according to Table 17, GBVSBP + MCG achieves a maximum score of 89.02, a value that is better than PiCANet and is between DHSNet and BASNet. Moreover, the results agree with those reported in Table 19, where DHSNet is better than GBVSBP but not compared to GBVSBP + MCG. Finally, the results with PASCAL-S show the poor performance of GBVSBP, but considering the expected improvement after combining it with the segmentation process, we could expect at least to overpass DHSNet as exemplified in the experiments provided in Figure 3, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **GA** & **PSO** & **GPMCC** & **GPSED** & **GBVSBP + MCG** \\ \hline MSRA-BTest & 0.7579 & 0.7455 & 0.7711 & 0.7662 & **0.8308** \\ ECSSD & 0.6200 & 0.5988 & 0.6592 & 0.6592 & **0.7591** \\ SED2 & 0.6914 & 0.6701 & 0.7148 & **0.7340** & 0.6919 \\ iCoseg & 0.6678 & 0.6557 & 0.6865 & 0.7157 & **0.7168** \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Comparison with others models and databases published in [6]. Our proposal achieves better results than the more complex proposal using compound functions. Figure 4: Example of maps obtained in experiments whose results are controversial despite that our models observe better performance in the benchmark. The FT image shows the relevance of the segmentation process to rank highest in the benchmark. The PASCAL-S image shows a misleading ground truth that confuses all algorithms. Finally, the IMGSAL image shows a reasonably well-segmented image that contrasts with all others in the dataset, generating a controversial result. where the improvement was of more than 10 points. Beyond the statistical results, it is true that deep learning methods currently have the edge over other strategies, but most researchers take it for granted. As illustrated in this document, the community should include other approaches in the comparison, especially after considering other criteria, such as the robustness of SOD against adversarial attacks [45]. Figure 5: Brain programming result of the dorsal stream using the best GBVS + MCG program.