# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 2 Related Work
In [6], the contribution is oriented toward the mechanical design of combination models using genetic programming. The proposed approach automatically selects the algorithms to be combined, and the combination operators use a set of candidate saliency detection methods and a set of combination operators as input. This idea follows a long history in computer vision about combination models. To achieve good results, the authors rely on complex algorithms, such as DHSNet, as building blocks to the detriment of transparency since the method does not enhance the complex algorithms in the function set but only the output. Since fixation prediction algorithms are complex heuristics, another alternative is to work directly with some essential parts of the algorithm to attempt to improve/discover the whole design. In [5], genetic programming generates visual attention models--fixation prediction algorithms--to tackle salient object segmentation. However, the authors took a step back, returning to the first stage--theoretical transparency--and revisited [PERSON] et al., looking for a suitable model susceptible to optimization [34]. [PERSON] et al. develop an optimization-based approach introducing symbolic learning within crucial parts of the complete model, using it as a basic algorithm that serves the purpose of a template. This algorithm uses as a foundation the code reported in [PERSON]'s work [35]. In this way, [PERSON] et al. attempt to fulfill the second stage--algorithmic transparency--since they contemplate the difficulty of articulating the whole design exposed by [PERSON] and [PERSON] through heuristic search. In summary, it is difficult to delegate all practical aspects to the computer according to the genetic programming paradigm. This way of looking for visual attention programs has already impacted practical applications, such as visual tracking [36; 37]. This method searches for new alternatives in the feature integration theory (FIT) processes. That in cludes processes for acquiring visual features, the computation of conspicuity maps, and integrating features. Nevertheless, a drawback is that the visual attention models evolved to detect a particular/single object in the image. In this work, we would like to identify all foreground regions and separate them from the background. Note that the foreground can contain any object on a particular database. Proper identification of the foreground from the background was the problem approached by [PERSON] et al. and is known as salient object detection, a hot topic exclusively studied nowadays from a deep learning viewpoint. The idea is to replace [PERSON]'s algorithm with the proposal published in [38] and the further adaptation and benchmark described in [30]. We corroborated the results, testing with more databases [24] and deep learning methodologies [27]. We highlight the following contributions:  
1. The study of salient object detection using a symbolic approach methodology provides a way to extend previous human-made designs with learning capabilities. The algorithms defeat DHSNet and obtain competitive outcomes against recent deep learning models. 2. Learning is an inductive process, and as with any heuristic/stochastic method, we need methodologies that corroborate the results through statistical approaches. We use k-fold cross-validation to provide evidence about the proposed methodology's robustness. 3. In BP, the template refers to a deductive reasoning strategy combined with an inductive learning approach powered by genetic programming. The whole methodology describes an inferential knowledge system that naturally fulfills the requirement of being explainable. The programs evolved by artificial evolution complete the flowchart following logical reasoning. [PERSON] and coworkers adapt [PERSON] and [PERSON]'s theory into essential computational reasoning [34]. [PERSON]'s algorithm accomplishes two stages: (1) visual feature acquisition and (2) feature integration.