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Salient object detection, as a necessary step of many computer vision applications, has attracted extensive attention in recent years. A novel salient object detection method is proposed based on multi‐superpixel‐scale contrast. Saliency value of each superpixel is measured with a global score, which is computed using the region's colour contrast and the spatial distances to all other regions in the image. High‐level information is also incorporated to improve the performance, and the saliency maps are fused across multiple levels to yield a reliable final result using the modified multi‐layer cellular automata. The proposed algorithm is evaluated and compared with five state‐of‐the‐art approaches on three publicly standard datasets. Both quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of the proposed method.