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Despite recent improvements in computer vision, artificial visual systems'design is still daunting since an explanation of visual computing algorithmsremains elusive. Salient object detection is one problem that is still open dueto the difficulty of understanding the brain's inner workings. Progress on thisresearch area follows the traditional path of hand-made designs usingneuroscience knowledge. In recent years two different approaches based ongenetic programming appear to enhance their technique. One follows the idea ofcombining previous hand-made methods through genetic programming and fuzzylogic. The other approach consists of improving the inner computationalstructures of basic hand-made models through artificial evolution. Thisresearch work proposes expanding the artificial dorsal stream using a recentproposal to solve salient object detection problems. This approach uses thebenefits of the two main aspects of this research area: fixation prediction anddetection of salient objects. We decided to apply the fusion of visual saliencyand image segmentation algorithms as a template. The proposed methodologydiscovers several critical structures in the template through artificialevolution. We present results on a benchmark designed by experts withoutstanding results in comparison with the state-of-the-art.

Automated Design of Salient Object Detection Algorithms with Brain Programming