We focus on saliency estimation in digital images. We describe why it is important to adopt a data-driven model for such an illposed problem, allowing for a universal concept of “saliency” to naturally emerge from data that are typically annotated with drastically heterogeneous criteria. Our learning-based method also involves an explicit analysis of the input at multiple scales, in order to take into account images of different resolutions, depicting subjects of different sizes. Furthermore, despite training our model on binary ground truths only, we are able to output a continuous-valued confidence map, which represents the probability of each image pixel being salient. Every contribution of our method for saliency estimation is singularly tested according to a standard evaluation benchmark, and our final proposal proves to be very effective in a comparison with the state-of-the-art.