Saliency Detection Based on Feature Fusion and Weighted Hypergraph

In view of the common problems in image saliency detection, such as inaccurate positioning of saliency objects and easy loss of detail information in complex scenario. This paper proposes a saliency detection based on feature fusion and weighted hypergraph. Firstly, SLIC superpixel segmentation is performed to extract the color features, spatial features and deep features of the image, and a weighted hypergraph model is constructed. Then the saliency score of each superpixel block is obtained by using the random walk algorithm, the primary saliency map is generated according to the order of each superpixel score. Compared with seven advanced algorithms on three challenging datasets (ECSSD, HKU-IS, PASCAL-S), the proposed model significantly improves the performance of saliency map details and salient object localization under complex scenario.