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Discriminant saliency is a kind of decision-theoretic-based saliency detection method that has been proposed recently. Based on local gradient distribution, this paper proposes a simple but efficient discriminant centre-surround hypothesis, and builds local and global saliency models by combining multi-scale intensity contrast with colour and orientation features. This method makes three important contributions. First, a circular and multi-scale hierarchical centre-surround profile is designed for the local saliency detection. Secondly, the dense local gradient orientation histogram (LGOH) of the centre-surround region is counted and used for the local saliency analysis. And thirdly, a new integration strategy for the local and global saliency is proposed and applied to the final visual saliency discriminant. Experiments demonstrate the effectiveness of the proposed method. Compared with 12 state-of-the-art saliency detection models, the proposed method outperforms the others in precision-recall, F-measures and mean absolute error (MAE), and can produce a more complete salient object.