Semi-supervised saliency classifier based on a linear feedback control system model
Shuwei Huo, Yuan Zhou, Sun‐Yuan Kung · 2017
Linear feedback control systems (LFCS) are amenable to numerous object recognition and detection tasks on account of its functional properties in signal filtering and error correction. In fact, there exists an intimate relationship between control states and salient values. Therefore, we propose a novel semi-supervised classifier which makes use of linear feedback control theory to improve saliency detection performance. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which may lead to the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using a LCSF model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. Based on our experiments on public datasets, it can be demonstrated that our approach significantly outperforms the state-of-the-art methods.