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A salient object detection framework using linear quadratic regulator controller

机译：使用线性二次调节器控制器的突出对象检测框架

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摘要

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 different strategies. Doing so, we would ultimately have more effective distinction between those regions. After the initialization step, the control systems refine both estimations in parallel until reaching a steady state for each of them. Within the mentioned process, by using optimal control concept, specifically LQR controller (for the first time in the field), 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 refined 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.
机译：本文提出了一种基于线性二次调节器（LQR）控制器的新型突出物体检测框架。该研究的主要目标是利用最佳控制理论来提高检测图像中突出物体的性能。在这方面，为了检测突出和非凸极区域，采用了两个基于LQR的控制系统。在拟议的框架中，为了初始化控制系统，使用两种不同的策略已经完成了背景和前景估计。这样做，我们最终会在这些地区之间更有效地区分。在初始化步骤之后，控制系统并行地细化两个估计，直到达到每个估计。在提到的过程中，通过使用最优控制概念，特别是LQR控制器（第一次在现场中），负责确定显着性值的控制信号将不断优化。最后，将通过背景和前景优化的初始映射组合生成原始显着性图。最后，将通过使用角嵌入方法来改进集成显着性图。三个基准数据集上的实验评估显示，所提出的框架表现良好，并通过基于深度学习的方法引入了可比的结果。

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