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Automatic detection of salient objects in visual media (e.g., videos and images) has been attracting much attention. The detected salient objects can be utilized for segmentation, recognition, and retrieval. However, the accuracy of saliency detection remains a challenge. The reason behind this challenge is mainly due to the lack of a well-defined model for interpreting saliency formulation. To tackle this problem, this letter proposes to detect salient objects based on selective contrast. Selective contrast intrinsically explores the most distinguishable component information in color, texture, and location. A large number of experiments are thereafter carried out upon a benchmark dataset, and the results are compared with those of 12 other popular state-of-the-art algorithms. In addition, the advantage of the proposed algorithm is also demonstrated in a retargeting application.