Salient object detection based on global multi-scale superpixel contrast

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.

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