# Visual Saliency Detection Using a Rule-Based Aggregation Approach
## 3 Experimental Results
### Datasets and Quantitative Metrics  
In this work, we used three benchmark datasets typically used to evaluate the performance of salient object detection methods: MSRA1K [15], ECSSD [46], and iCoseg [47]. We used such datasets since they have been used in diverse research works, such as those by [PERSON] et al. [48], and [PERSON] et al. [49], containing one or multiple salient objects under complex scenarios. Furthermore, the ground truth is available and given at the pixel level. The MSRA1K [15] dataset contains 1000 images with unambiguous salient objects under a diversity of scenes, with the binary annotations at the pixel level. ECSSD [46] dataset includes 1000 images with the binary pixel-wise annotations of the salient object. This dataset contains images mainly of natural scenarios with a cluttered background. The iCoseg [47] dataset includes 643 images of a wide variety of scenarios. In addition, this dataset includes the binary pixel-wise segmentation of one or multiple salient objects present in the images. The object segmentation was done by a human user. In Figure 4, we show three sample images of each database and their corresponding ground truth.  
Figure 3: Ten samples out of the complete set of rules obtained from MODLEM algorithm.  
For a quantitative evaluation of the RSD method performance, we adopted, in the evaluation setup, the F-measure metric. Originally, the F-measure was introduced for evaluation of information extraction technology [50]. The F-measure is frequently used to measure performance in a variety of prediction problems [51], such as binary classification and multi-label classification. Considering saliency detection as a binary classification task, the F-measure has been adopted by diverse authors to evaluate saliency detection models.  
The F-measure depends on two metrics: precision and recall. In the saliency detection task, the precision value is defined as the ratio of correctly assigned salient pixels and the total number of salient pixels predicted, while the recall is defined as the proportion of detected salient pixels and the total number of salient pixels according to the ground truth. The precision and recall are defined as in Equations (13) and (14), respectively.  
\[precision=\frac{true\_positive}{true\_positive+false\_positive}, \tag{13}\]  
\[recall=\frac{true\_positive}{true\_positive+false\_negative}. \tag{14}\]  
The F-measure is defined as the weighted harmonic mean of precision and recall metrics, with a non-negative weight of \(\beta\). In Equation (15) we give the formulation of F-measure.  
\[F_{\beta}=\frac{(1+\beta^{2})precision\times recall}{\beta^{2}\times precision+ recall}, \tag{15}\]  
where \(\beta\) is a parameter to control the weight of precision and recall metrics. Following the arguments presented by [PERSON] et al. [48], where it is stated that precision is more important than recall, and in a similar manner as in [PERSON] et al. [15], we set \(\beta^{2}\) with a fixed value of 0.3 to weight precision more than recall.  
Figure 4: Three sample images of each dataset used and their corresponding ground truth: (**a**) MSRA1K dataset; (**b**) ECSSD dataset; and (**c**) iCoseg dataset.