There is an emerging interest on using background prior in saliency detection. However, these methods fail to locate the position of background accurately. In this paper, a novel saliency detection approach which chooses more precise background regions is proposed. First, in order to pick out the real background from the boundary of the image, the background probability is measured by boundary ratio. Next, according to the geodesic distance to background regions, the edge saliency map and color saliency map are calculated in the Edge and RGB-LAB-XY feature space, respectively. Furthermore, combining the saliency cues by using an energy function, the final saliency map is generated. The proposed model has the following two advantages: the erroneous background removal guarantees the accuracy of background and the detection of objects located at the boundary of image; the energy minimization enable the detected objects to be more complete and edges of targets to be clearer. Comprehensive experiments on two benchmark datasets demonstrate the superiority of the proposed algorithm over the 5 state-of-the-art methods.