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The saliency detection technologies are very useful to analyze and extract important information from given multimedia data, and have already been extensively used in many multimedia applications. Past studies have revealed that utilizing the global cues is effective in saliency detection. Nevertheless, most of prior works mainly considered the single-scale segmentation when the global cues are employed. In this paper, we attempt to incorporate the multi-scale global cues for saliency detection problem. Achieving this proposal is interesting and also challenging (e.g., How to obtain appropriate foreground and background seeds effectively? How to merge rough saliency results into the final saliency map efficiently?). To alleviate the challenges, we present a three-phase solution that integrates several targeted strategies, first, a self-adaptive strategy for obtaining appropriate filter parameters; second, a cross-validation scheme for selecting appropriate background and foreground seeds; and third, a weight-based approach for merging the rough saliency maps. Our solution is easy to understand and implement, but without loss of effectiveness. Extensive experimental results based on benchmark datasets demonstrate the feasibility and competitiveness of our proposed solution.
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