Video salient object detection via cross-frame cellular automata

Salient object detection aims to detect the attractive objects on images and videos. In this paper, we propose a novel salient object detection method for videos based on cross-frame cellular automata. Given a video, we first represent the video frames with super-pixels, and construct a saliency propagation network among super-pixels within a frame and between adjacent frames based on their appearance similarities and temporal coherency. Second, we initialize the saliency map of each frame with the fusion of two saliency maps generated by appearance and motion features independently. Finally, we utilize cellular automata updating to propagate saliency among super-pixels iteratively and generate the coherent saliency maps with complete objects. The experimental results show that our method outperforms the state-of-the-art methods on different types of videos.