Saliency detection framework via linear neighbourhood propagation

In this study, a novel saliency detection algorithm based on linear neighbourhood propagation is proposed. The proposed algorithm is divided into three steps. First, the authors segment an input image into superpixels which are represented as the nodes in a graph. The weight matrix of the graph, which indicates the similarities between the nodes, is calculated by linear neighbourhood reconstruction. Second, the nodes, which are located at top, bottom, left and right of image boundary, are labelled as boundary priors. Then, based on weight matrix, label propagation is used to propagate the labels to unlabelled nodes. They rank the nodes according to the label information and select the nodes with minor information as saliency priors. Last, based on saliency priors, saliency detection is carried out by label propagation again. The nodes with more information are considered as saliency regions. Experimental results on three benchmark databases demonstrate the proposed method performs well when it is against the state‐of‐the‐art methods in terms of accuracy and robustness.