# Visual Saliency Detection Using a Rule-Based Aggregation Approach
## 3 Experimental Results
### Evaluation
#### 3.3.2 Saliency Detection Comparison Using an Adaptive Threshold  
In our first experiment, we performed a comparison of the proposed RSD method and other methods on each dataset. To compute precision, recall, and F-measure, we needed to binarize the saliency maps from saliency models. We binarized the maps obtained from individual methods by using the adaptive threshold.From each binarized image, we computed precision, recall, and F-measure metrics. The overall performance on each dataset was estimated by averaging these metrics obtained from each image. The obtained results on the three datasets are depicted in Figure 6. In the MSRA1K dataset, our RSD model outperformed other models; the F-Measure obtained by our model was 0.897 while the SF model, which is the most proximate model, obtained 0.858. The highest precision value was achieved by our approach, which scored 0.929, while the most proximate model, SF, obtain a precision value of 0.879. The recall measure obtained by our model was 0.723, and the PW model attained the highest recall measure of 0.927. From the results obtained on iCoseg dataset, we observed that our proposal obtained an F-measure value of 0.736, whereas the model RC achieved 0.717. Our proposal obtained the highest precision value of 0.826 while the SF model performed 0.749. The PW model obtained the highest recall value of 0.756. Finally, the results obtained in ECSSD dataset indicate that the RC model obtained the highest F-measure value of 0.708, while our approach scored 0.679. In contrast, our proposal attained the highest precision value of 0.789 while the RC model achieved a precision value of 0.738. The PW model obtained the highest recall measure of 0.700. \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Superior**} \\ \hline
**Combination** & **F-Measure** \\ \hline RC & 0.633 \\ RC, SF & 0.663 \\ RC, SF, MDC & 0.715 \\ RC, SF, MDC, MBS & **0.736** \\ RC, SF, MDC, MBS, FT & 0.730 \\ RC, SF, MDC, MBS, FT, HC & 0.726 \\ RC, SF, MDC, MBS, FT, HC, CA & 0.725 \\ \hline \multicolumn{2}{c}{**Inferior**} \\ \hline
**Combination** & **F-Measure** \\ \hline CA & 0.191 \\ CA, HC & 0.547 \\ CA, HC, FT & 0.566 \\ CA, HC, FT, MBS & 0.644 \\ CA, HC, FT, MBS, MDC & 0.667 \\ CA, HC, FT, MBS, MDC, SF & 0.687 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of various saliency models combinations. The combination that includes the RC, SF, MDC, and MBS models obtains the better performance in F-measure score. In Table 3, we present the overall performance of our RSD model and the rest of the models by averaging the results obtained from the adaptive thresholding test on the three datasets used. As shown in the table, our proposal obtained the highest F-measure of 0.770. In the case of the precision metric, the proposed model obtained the highest precision performance of 0.847. The RC model attained 0.759 and 0.780 for F-measure and precision, respectively. The highest recall of 0.794 was obtained by PW model, whereas our model performed 0.513. The model that obtained the highest score on each metric is highlighted in bold. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & \begin{tabular}{c} **Average** \\ **F-Measure** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Recall** \\ \end{tabular} \\ \hline FT (2009) & 0.577 & 0.624 & 0.481 \\ CA (2010) & 0.505 & 0.540 & 0.489 \\ RC (2011) & 0.759 & 0.780 & 0.737 \\ HC (2011) & 0.572 & 0.599 & 0.559 \\ SF (2012) & 0.722 & 0.760 & 0.589 \\ PW (2013) & 0.734 & 0.744 & **0.794** \\ MBS (2015) & 0.699 & 0.736 & 0.642 \\ MDC (2017) & 0.726 & 0.765 & 0.640 \\ RSD (ours) & **0.770** & **0.847** & 0.513 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average performance on the three datasets used. The model with the highest performance on each metric is highlighted in bold.