A computational model of visual attention using visual inferences is proposed. The dominant depth and the horizon line position are inferred from low-level visual features. This prior knowledge helps to find salient areas on still color pictures. Regarding the dominant depth, the idea is to favor the lowest spatial frequencies on close-up scenes whereas the highest spatial frequencies are used to predict salient areas on panoramic view. Some studies showed that the horizon line is a natural attractor of our gaze. Horizon detection is then used to improve the saliency prediction. Results show that the proposed model outperforms existing approaches. However, the dominant depth does not bring any gain in the saliency prediction.