Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model

Saliency detection aims to detect the most attractive objects in images, which has been widely used as a foundation for various multimedia applications. In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel prior. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on a center saliency prior and a dark channel prior. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. The proposed algorithm is evaluated on two public RGB-D datasets, and the experimental results show that our method outperforms the state-of-theart methods.