# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 1 Introduction  
Saliency is a property found in the animal kingdom whose purpose is to select the most prominent region on the field of view. Elucidating the mechanism of human visual attention including the learning of bottom-up and top-down processes is of paramount importance for scientists working at the intersection of neuroscience, computer science, and psychology [1]. Giving a robot/machine this ability will allow it to choose/differentiate the most relevant information. Learning the algorithm for detecting and segmenting salient objects from natural scenes has attracted great interest in computer vision [2], instrumentation and measurement [3], pattern analysis [4], and recently by people working with genetic programming [5, 6]. While many models and applications have emerged, a deep understanding of the inner workings remains lacking. This work develops over arecent methodology that attempts to design brain-inspired models of the visual system, including dorsal and ventral streams [7,8]. The dorsal stream is known as the \"where\" or \"how\" stream. This pathway is where the guidance of actions and recognizing objects' location in space is involved and where visual attention occurs. The ventral stream is known as the \"what\" stream. This pathway is mainly associated with object recognition and shape representation tasks. This work deals with the optimization/improvement of an existing algorithm (modeling the dorsal stream) and allows evolution to improve this initial template method. The idea is to leverage the human designer with the whole dorsal stream design's responsibilities by focusing on the high-level concepts, while leaving the computer (genetic programming--GP) with the laborious chore of providing optimal variations to the template. Therefore, the human designer is engaged in the more creative process of defining a family of algorithms [9]. Figure 1 shows the template's implementation (individual representation) that emulates an artificial dorsal stream (ADS). As we can observe, the whole algorithm represents a complex process based on two models. A neurophysiological model called the two-pathway cortical model--the two-streams hypothesis--and a psychological model called feature integration theory [10]. This last theory states that human beings perform visual attention in two stages. The first is called the preattentive stage, where visual information is processed in parallel over different feature dimensions that compose the scene: color, orientation, shape, intensity. The second stage, called focal attention, integrates the extracted features from the previous stage to highlight the scene's region (salient object). Hence, the image is decomposed into several dimensions to obtain a set of conspicuity maps, which are then integrated--through a function known as evolved feature integration (EFI)--into a single map called the saliency map. Brain programming (BP) is based on the most popular theory of feature integration for the dorsal stream and the hierarchical representation of multiple layers as in the ventral stream [11]. Note that the template's design can be adapted according to the visual task. In this work, we focus on designing an artificial dorsal stream. Moreover, BP replaces the data-driven models with a function-driven paradigm. In the function-driven process, a set of visual operators (\(VOs\)) is fused by synthesis to describe the image's properties to tackle object location and recognition tasks. Intelligence data analysis (IDA) is a new research area concerned with analyzing data science effectively to discover meaningful information to improve decisions for real-world problems effectively [12]. Traditionally, deductive reasoning is the method of preference since problems can be naturally explained under pure reasoning, and salient object detection is no exception. However, inductive reasoning, largely represented under the umbrella of deep learning, is the flagship approach of a new wave of methodologies following the design by synthesis [13].