Automated Design of Salient Object Detection Algorithms with Brain Programming

TL;DR

This paper introduces a novel brain-inspired genetic programming approach to automatically design salient object detection algorithms, combining fixation prediction and image segmentation, achieving state-of-the-art results.

Contribution

It expands the artificial dorsal stream using evolutionary methods to automatically discover effective structures for salient object detection.

Findings

Achieved outstanding results on a benchmark dataset.

Discovered critical structures through artificial evolution.

Outperformed existing state-of-the-art methods.

Abstract

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