Dempster‐Shafer theory‐based hierarchical saliency detection

This is the authors' abstract. We don't add key points for this paper.

This article presents a new saliency detection method based on a hierarchical structure. In order to better capture the key elements in an image, we first construct a three‐layer structure by applying a merging strategy to the image. For different layers, we exploit different methods to detect salient regions. After obtaining initial saliency maps of five layers, we construct a Dempster‐Shafer model to fuse and refine these saliency maps. In the Dempster‐Shafer model, the saliency value of upper layers can provide guidance for saliency detection of lower layers. Finally, we fuse the saliency map of each layer to obtain the final result. Abundant experimental results on two benchmark data sets demonstrate that our method outperforms most of 12 state‐of‐the‐art methods in terms of three popular evaluation criteria, that is, the P ‐ R curve, the F ‐measure, and the mean absolute error.