Visual context is used in different forms for saliency computation. While its use in saliency models for fixations prediction is often reasoned, this is less so the case for approaches that aim to compute saliency at the object level. We argue that the types of context employed by these methods lack clear justification and may in fact interfere with the purpose of capturing the saliency of whole visual objects. In this paper we discuss the constraints that different types of context impose and suggest a new interpretation of visual context that allows the emergence of saliency for more complex, abstract, or multiple visual objects. Despite shying away from an explicit attempt to capture "objectness" (e.g., via segmentation), our results are qualitatively superior and quantitatively better than the state-of-the-art.
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