The existing salient region detection algorithms based on visual stimulus and prior knowledge are difficult to detect some complicated salient regions.The human vision system can distinguish these complicated salient regions because of the rich semantic knowledge in the human visual system.We construct a semantic salient region detection network using the fully convolutional structure.Learning the mapping from the low-level features to the human semantic cognition

our network can extract semantic salient region effectively.Aiming to the defects of the semantic salient region map

we introduce the color information

object boundary information and spatial consistency information to derive accurate superpixel-level foreground and background probability.At last

we fuse the foreground and background probability

semantic information and spatial consistency information to derive the final salient region map.The experiments comparing with the state-of-the-art 15 algorithms on 6 data sets demonstrate the effectiveness of our algorithm.