# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 2 Related Work  
For a learning algorithm design technique to be well received, it needs to solve several analysis levels regarding the automated generation or improvement of algorithms for solving a particular or broad set of problems [22]. A significant critique of deep learning is the opacity generating problems, such as lack of interpretability, reusability, and generality. Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Here, we follow a strategy for increasing transparency based on three levels of explanation about what vision is, how it works, and why we still lack a general model, solution, or explanation for artificial vision [23]. The idea follows a goal-oriented framework where we study learning as an optimization process [16]. The first is theoretical transparency or knowledge of the visual information processing whose design is the computation goal. The second is algorithmic transparency or knowledge of visual processing coding. Finally,  
Figure 1: Brain programming implementation of the dorsal stream using the combination of visual saliency and image segmentation algorithms. We propose discovering a set of visual operators (_VOs_) and the evolutionary feature integration (EFI) within the template through artificial evolution. The whole design makes a design balance between the human designer (deductive reasoning) and the computer (symbolic learning). the third level is execution transparency, or knowledge of implementing the program considering specific hardware and input data. Visual attention has a long history, and we recommend the following recent articles to the interested reader to learn more about the subject [24; 25; 26; 27]. However, to put it into practice, it is better to look for information about benchmarks [28; 29; 30]. In the present work, we select the study of [PERSON] et al. since it provides an extensive evaluation of fixation prediction and salient object segmentation algorithms, as well as statistics with standard datasets [30]. They provide a framework focusing on the performance of GBVS against several state-of-the-art proposals. The study also explains how fixation prediction algorithms adapt to salient object detection by incorporating a segmentation stage. Fixation prediction algorithms target predicting where people look in images. Salient object detection focus on a wide range of object-level computer vision applications. Since fixation prediction originated from cognitive and psychological communities, the goal is to understand the biological mechanism. Salient object detection does not necessarily need to understand biological phenomena. Regarding the second level of explanation (algorithmic transparency) or the knowledge of the visual processing coding, we can observe two different approaches to incorporating learning into such a study. A deep learning technique exemplifies the first (DHSNet--deep hierarchical saliency network [31]) since it is used as a building block in [6]. This method is a fully convolutional network (FCN), a well-known method designed to address the limitations of multi-layer perceptron (MLP)-based methods. FCN architectures lead to end-to-end spatial saliency representation learning and fast saliency prediction within a single feed-forward process. FCN-based methods are now dominant in the field of computer vision. Since DHSNET results rank worst in the proposed benchmark, we decided to train and test two more FCN approaches:  
* BASNet--boundary-aware salient object detection [32];
* PiCANet-learning pixel-wise contextual attention for saliency [33]. Furthermore, we repeat the experiments to compare our proposal with state-of-the-art methodologies to know the limitations of the proposed method described in this paper. The second methodology is represented by evolutionary computation, applying genetic programming. We identify two representative works.