Abstract

In this paper, a novel salient object detection framework based on Linear Quadratic Regulator (LQR) controller is proposed. The major goal of this research is to take advantage of optimal control theory for improving the performance of detecting salient objects in images. In this regard, for the sake of detection of salient and non-salient regions, two LQR-based control systems are employed. In the proposed framework, for the initialization of the control systems, background and foreground estimations have been done with two di erent strategies. Doing so, we would ultimately have more e ective distinction between those regions. After the initialization step, the control systems re ne both estimations in parallel until reaching a steady state for each of them. Within the mentioned process, by using optimal control concept, speci cally LQR controller (for the rst time in the eld), control signals which are in charge of determining saliency values, would be constantly optimized. At the end, the raw saliency map will be generated by combination of background and foreground optimized initial maps. Finally, the integrated saliency map will be re ned by using angular embedding method. The experimental evaluations on three benchmark datasets shows that the proposed framework performs well and introduces comparable results with some deep learning based methods.