# Visual Saliency Detection Using a Rule-Based Aggregation Approach
Note that the goal of these techniques is to exploit the outcomes of different individual methods to generate a new saliency map, which gives a more accurate saliency estimation. In the research work of [PERSON] et al. [28], a data-driven combination approach based on the conditional random field (CRF) framework is presented. Based on this framework, [PERSON] considered the interaction between neighboring pixels and modeled the contribution of each individual saliency map. In addition, [PERSON] presented in the same research work the Pixel-Wise aggregation model (PW). In this approach, the aggregation is performed by weighting diverse results of individual saliency detection models. The weights are learned by using a standard logistic regression. Although these approaches are found to be effective in distinct cases, they present failures when detecting ambiguous cases, since the combination criterion is not flexible. Additionally, the output of these methods still needs a binarization stage. Recently, deep learning has been used to address saliency detection tasks. In the proposal of [PERSON] et al. [29], a deep neural network is used to estimate saliency locally. Then, the obtained saliency map is refined by exploiting high-level object concepts. [PERSON] et al. [30] employed global and local context in a unified multi-context convolutional neural network. [PERSON] and [PERSON] [31] proposed a two-stage saliency detection frame based on convolutional neural networks. The architecture proposed estimates saliency by learning from global saliency cues. Then, a refinement process is carried out incorporating local context information. To obtain more accurate boundaries and compact saliency maps, [PERSON] et al. [32] proposed a deep network incorporating a level set function. Besides, a superpixel-based guided filter is incorporated as a layer of the network, allowing to obtain a full resolution saliency map. [PERSON] et al. [33] applied attention weight in a top-down manner to filter out a noisy response from the background. In addition, a saliency refinement network is proposed to improve the resolution of the saliency map by using a second-order term to introduce nonlinearity in the learning process. Despite the remarkable performance of the learning approaches mentioned above, the outcome obtained is still given as a gray-level saliency map. Furthermore, the neural network performs as black-box which makes difficult the human-comprehension of the obtained saliency model [34]. In this paper, we propose an aggregation system for salient object detection using individual descriptors in a rule-based approach. According to [PERSON] and [PERSON] [34], rules are one of the most popular representations of knowledge. The rules are more human-comprehensible than other representations of knowledge [35], which is useful when constructing intelligent systems. Specifically, we explore the use of rough-set-based rules for the saliency determination of a given pixel within an image. The rough set theory is a mathematical approach introduced by [PERSON] [36], useful for dealing with vagueness and uncertainty in data analysis. The rough sets are useful when it is not possible to represent a concept with a precise criterion [37]. According to [PERSON] and [PERSON] [38], rough sets discover patterns hidden in data. Additionally, the set of obtained rules gives an overall description of the data, eliminating redundancy present in the original data. Exploiting the main advantage of rough set theory, which is that it does not need any additional information about the data [39], we propose a combination of four different state-of-the-art methods as feature descriptors included in the rules: Saliency Filter (SF) [23], Minimum Barrier Salient Object Detection (MBS) [40], Region-based Contrast (RC) [22], and Minimum Directional Contrast (MDC) [25]. The selection criteria of these methods correspond to how saliency is computed. MDC and MBS estimate saliency in a pixel-wise manner, whereas SF and RC compute saliency in a region-based manner.