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In this paper, a new bottom-up visual saliency model is proposed. Based on the idea that locally contrasted and globally rare features are salient, this model will be called "RARE" in the following sections. It uses a sequential bottom-up features extraction where first low-level features as luminance and chrominance are computed and from those results medium-level features as image orientations are extracted. A qualitative and a quantitative comparison are achieved on a 120 images dataset. The RARE algorithm powerfully predicts human fixations compared with most of the freely available saliency models.

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