Salient object detection by aggregating contextual information

Salient object detection is a fundamental computer vision task. Deep learning techniques have been successfully applied for salient object detection, however, the saliency maps still suffer from incomplete predictions owing to the difficulty and complexity of objects. We propose a novel convolutional neural network based on aggregating contextual information that can improve the accuracy of salient object detection. In order to obtain high-level global information for fine-level feature maps, we add a pyramid pooling module to the proposed network. Moreover, building a guiding module that consists of four context-information feature modules, the algorithm can gain more effective information and enrich the details of saliency maps. Extensive experiments conducted using six benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art algorithms.