The modeling of visual attention has gained much interest during the last few years since it allows to efficiently drive complex visual processes to particular areas of images or video frames. Although the literature concerning bottom-up saliency models is vast, we still lack of generic approaches modeling top-down task and context-driven visual attention. Indeed, many top-down models simply modulate the weights associated to low-level descriptors to learn more accurate representations of visual attention than those ones of the generic fusion schemes in bottom-up techniques. In this paper we propose a hierarchical generic probabilistic framework that decomposes the complex process of context-driven visual attention into a mixture of latent subtasks, each of them being in turn modeled as a combination of specific distributions of low-level descriptors. The inclusion of this intermediate level bridges the gap between low-level features and visual attention and enables more comprehensive representations of the later. Our experiments on a dataset in which videos are organized by genre demonstrate that, by learning specific distributions for each video category, we can notably enhance the system performance.
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