# Visual Saliency Detection Using a Rule-Based Aggregation Approach
## 2 Methodology
### Feature Extraction  
Antecedents in rule-based approaches are features that describe a given object or class. The proposed RSD system uses grayscale saliency maps as input features. We utilize four state-of-the-art saliency detection models: Minimum Barrier Salient Object Detection (MBS) [40], Minimum Directional Contrast (MDC) [25], Region-based Contrast (RC) [22], and Saliency Filter (SF) [23]. As far as we know, these methods are the state-of-the-art of individual models for saliency detection. In addition, the selected methods are recent and achieve a good performance in the saliency detection task. The experimentation that led us to select these four saliency models for aggregation purposes is detailed in Section Below, we present a brief description of these saliency models. **Minimum Barrier Salient Object Detection. ** The saliency detection model proposed by [PERSON] et al. [40] aims to highlight a salient object within a scene. By using the minimum barrier distance, the Minimum Barrier Salient Object Detection (MBS) model exploits the boundary connectivity hint. Visiting each \(x\) pixel position on the image, each adjacent \(y\) pixel is taken into account to minimize the path cost at \(x\). The minimization function is defined in Equation (4). \[D(x)\leftarrow min\left\{ \begin{array}{l}D(x),\\ \beta_{I}(P(y)\cdot\langle x,y\rangle),\end{array} \right. \tag{4}\]  
where \(D(x)\) is the distance that is desired to calculate, \(P(y)\) represents the actual path assigned to pixel position \(y\) and \(\langle y,x\rangle\) is the existing edge from \(y\) to \(x\). Being \(P(y)\cdot\langle y,x\rangle\) denoted by \(P_{y}(x)\), the cost function proposed in this work is represented as in Equation (5). \[\beta_{I}(P_{y}(x))=\max\{U(y),I(x)\}-\min\{L(y),I(x)\}, \tag{5}\]  
where \(U(y)\) is the highest pixel value on \(P(y)\) and \(L(y)\) is the lowest pixel value on \(P(y)\). The saliency map obtained from this model is given as a gray-level image, where the intensity of each pixel represents the estimated saliency level. Hence, if a binary output is needed, thresholding shall be performed. **Minimum Directional Contrast. ** To detect salient objects in images, [PERSON] and [PERSON] [25] proposed the Minimum Directional Contrast (MDC) saliency detection model. The MDC model takesinto account the spatial distribution of contrast. The main contribution of this model is a metric to estimate saliency, which is called Minimum Directional Contrast (MDC). Saliency is estimated in a pixel-wise manner and using an input image in the CIELAB color space. The input image is divided into four regions, taking the inspected pixel \(x\) as the center of the image. Each region is a box delimited by four points: top left (TL), top right (TR), bottom left (BL), and bottom right (BR). Then, the directional contrast for each region is defined as in Equation (6). \[DC_{i,\Omega}=\sqrt{\sum_{j\in\Omega}\sum_{ch=1}^{K}(I_{i,ch}-I_{j,ch})^{2}}. \tag{6}\]  
In this research work, the authors introduced an interesting property about the distribution of contrast between regions and the examined pixel. If the pixel \(i\) belongs to the foreground of the image, the contrast in all directions is high. On the contrary, if the pixel belongs to the background of the image, the contrast is low in one direction. The raw saliency measure is estimated as the minimum contrast of the four regions, which is defined in Equation (7). \[ \begin{split} S(i)&=\min_{\Omega\in\,TL,BR,BL,BR}DC_ {i,\Omega}\\ &=\sqrt{\min_{\Omega\in\,TL,RR,BL,BR}\sum_{j\in\Omega}\sum_{ch=1 }^{K}(I_{i,ch}-I_{j,ch})^{2}}.\end{split}  \tag{7}\]  
Because the saliency map is given in grayscale, the authors proposed a saliency enhancement using a post-processing step. **Region-based Contrast. ** [PERSON] et al. [22] proposed the Region Based Contrast (RC) model to salient object detection. The RC method considers the contrast between regions to estimate saliency. Firstly, the incoming image in CIELAB color space is segmented into regions.