# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 5 Conclusions  
In this work, we propose a method to improve the ADS model presented by [5]. This method consists of applying an algorithm called GBVS that surpasses [PERSON]'s previous model. GBVS uses a graph-based approach using Markov chains while involving the same stages as the [PERSON] model. Moreover, we follow the idea of combining fixation prediction with a segmentation algorithm to obtain a new method called GBVSBP + MCG to tackle the problem of salient object segmentation. As we show in the experiments, the novel design scores highest in the FT, IMGSAL, and PASCAL-S datasets of a benchmark provided by [30]. These tests show the strength and generalization power of the discovered model compared to others developed manually and current CNNs, such as DHSNet, which is surpassed by more than 12 percentage points in FT. We also give the results on four datasets described in [6] with outstanding results. The results are revealing regarding the difficulty of solving this visual task. In the FT and PASCAL-S, objects are defined with accurate ground truth, and the algorithm GBVSBP + MCG improves the results of the original algorithm slightly. However, in the IMGSAL database, the ground truth is poorly segmented, and GBVSBP significantly improves the score compared to the original proposal. Therefore, we can say that there is a considerable benefit in combining analytical methods with heuristic approaches. We believe that this mixture of strategies can help find solutions to challenging problems in visual computing and beyond. One advantage is that the overall process and final designs are explainable, which is considered a hot topic in today's artificial intelligence. This research attempts to advance studies conducted by experts (neuroscientists, psychologists, and computer scientists) by adapting the symbolic paradigm for machine learning to find better ways of describing the brain's inner workings.  
Conceptualization, G.O. and R.O.; Data curation, G.O., J.A.M.-C., M.O., A.O. and R.P.; Formal analysis, G.O., A.O. and G.I.-V; Funding acquisition, G.O.; Investigation, G.O., J.A.M.-C., M.O., A.O., R.O. and R.P.; Methodology, G.O., J.A.M.-C. and R.P.; Project administration, G.O. and G.I.-V.; Resources, G.O., J.A.M.-C. and A.O.; Software, J.A.M.-C., M.O., A.O. and R.P.; Supervision, G.O.; Validation, G.O., J.A.M.-C., M.O., A.O., G.I.-V., R.O. and R.P.; Visualization, J.A.M.-C., M.O. and A.O.; Writing--original draft, G.O., J.A.M.-C., M.O. and A.O.; Writing--review and editing, G.O., G.I.-V. and R.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CICES grant number 634-135.  
Not applicable.  
The following project support this research: (1) Project titled \"Estudio de la programacion cerebral en problemas de reconocimiento a gran escal y sus aplicaciones en el mundo real\" CICESE-634135. We want to thank the editorial office of applied sciences and [PERSON] for approval of the full waiver of the article publishing charge.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Datasets**} \\  & \multicolumn{2}{c}{**FT**} & \multicolumn{2}{c}{**IMGSAL**} & \multicolumn{2}{c}{**PASCAL-S**} \\ \hline  & Avg. & \(\sigma\) & Avg. & \(\sigma\) & Avg. & \(\sigma\) \\ BP & 73.84 & 12.48 & 62.66 & 23.96 & 62.94 & 20.89 \\ BASNet & 92.67 & 12.51 & 63.75 & 31.73 & 80.20 & 21.15 \\ PiCANet & 81.45 & 15.27 & 71.60 & 22.16 & 75.81 & 19.80 \\ DrHSNet & 88.63 & 12.43 & 53.63 & 24.85 & 68.14 & 21.03 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Testing BP with GBVS against deep learning models for all databases. Since we are not combining the best programs with the segmentation step, the results are less performing for FT and PASCAL-S. However, this strategy is better for IMGSAL, as explained earlier. In general, we can say that the proposal is competitive compared to deep learning.