Concerning that the existing methods cannot well detect the salient object boundary and entire region, a new method based on super-pixel segmentation was proposed. Firstly, the bilateral filtering was employed on original images to reduce the local color difference and make the image smoother and more homogeneous; at the same time, the information of salient object edge was retained. The initial detection of salient object's edge was implemented by calculating the pixel' difference within the local window; super-pixel segmentation was adopted to filtered image so that the pixels with the same or similar color were divided into the same super-pixel block, based on this, the local contrast, global contrast and spatial distribution of super-pixel block were considered synthetically to calculate the salient value of each super-pixel block. Finally, the results of the above two parts were fused and optimized by guided filtering. The experiments were conducted on the international open data set MSRA-1000 compared with other seven methods. The average accuracy rate, average recall, and F-measure value of the proposed method are 81.57%, 77.13% and 80.50% respectively. The experimental results show that the proposed method can exact salient object in images effectively and robustly.