| Summary: | Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high‐contrast background, but they have no effect on the extraction of a salient object from images with complex low‐contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics‐like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low‐contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low‐contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state‐of‐the‐art approaches, the proposed approach performs well.
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