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To obtain a saliency map close to the saliency object as much as possible, an improved bottom-up visual attention model is presented. Firstly, early visual features such as intensity, color and orientation are extracted from an input image at multiple scales; Secondly, three conspicuity maps are created respectively according to early features; Thirdly, three conspicuity maps are combined into a saliency map nonlinearly. In the last step, different from Itti's model, the contribution rate of each conspicuity map to the saliency map is done in inversely proportional to the saliency points area. A set of experiments were carried out to demonstrate the effectiveness of the proposed model. The experimental results show that the algorithm is effective and saliency map accuracy increased by 15-20% compared to Itti's model.