In order to extract the salient region accurately from the image with complex background, we propose a saliency detection method based on the objectness foreground object and background prior (OFOBP). Firstly, the image is segmented by superpixels. The superpixel color spatial distribution of the image is calculated, and the initial saliency map is obtained. A certain number of target windows with corresponding target scores are obtained by the method of binarized normed gradients algorithm, and at the same time, the target windows are used to establish search areas. The multiwindow features are used to make foreground object forecast for superpixels so that the foreground saliency map is obtained. Secondly, the background template is established, and the background prior map is obtained by using the sparse reconstruction error. Finally, the two saliency maps are fused to get the final detection result. The effectiveness of the proposed method is verified by comparing it with other eleven algorithms in public data sets. The proposed algorithm can detect the salient regions more accurately, especially when dealing with the multiple salient object images with complex background.