Salient object detection via a linear feedback control system
Shuwei Huo, Yuan Zhou, Sun‐Yuan Kung · 2017
Linear feedback control systems (LFCS) have been widely applied in signal analysis, filtering, and error correction. Many functional properties of LFCS are amenable to numerous object recognition and detection tasks. In fact, there exists an intimate relationship between control states and salient values. This prompts us to adopt the linear feedback control system to detect salient object in static images. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. In addition, to initialize the system, we propose the so called boundary homogeneity based on a priori knowledge on the boundary to estimate the background likelihood and indirectly depict a foreground (saliency) map. By our experimental results, we demonstrates that such feedback control model can bring about noticable improvement in salient object detection.