Background prior has been widely used in many salient object detection models with promising results. These methods assume that the image boundary is all background. Then, color feature based methods are used to extract the salient object. However, such assumption may be inaccurate when the salient object is partially cropped by the image boundary. Besides, using only color feature is also insufficient. We present a novel salient object detection model based on background selection and multi-features. Firstly, we present a simple but effective method to pick out more reliable background seeds. Secondly, we utilize multi-features enhanced graph-based manifold ranking to get the saliency maps. Finally, we also present the salient object segmentation via computed saliency map. Qualitative and quantitative evaluation results on three widely used data sets demonstrate significant appeal and advantages of our technique compared with many state-of-the art models.