Salient object detection aims to detect the main objects in the given image. In this paper, we proposed an approach that integrates semantic priors into the salient object detection process. The method first obtains an explicit saliency map that is refined by the explicit semantic priors learned from data. Then an implicit saliency map is constructed using a trained model that maps the implicit semantic priors embedded into superpixel features with the saliency values. Next, the fusion saliency map is computed by adaptively fusing both the explicit and implicit semantic maps. The final saliency map is eventually computed via the post-processing refinement step. Experimental results have demonstrated the effectiveness of the proposed method, particularly, it achieves competitive performance with the state-of-the-art baselines on three challenging datasets, namely, ECSSD, HKUIS, and iCoSeg.