Saliency detection has been a hot topic in computer vision and image processing communities. Utilizing the global cues has been shown effective in saliency detection, whereas 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 (MSGC) for saliency detection. Achieving this proposal is interesting and also challenging (e.g., how to obtain appropriate foreground and background seeds; how to merge rough saliency results into the final saliency map efficiently). To alleviate various challenges, we present a solution that integrates three targeted techniques: (i) a self-adaptive approach for obtaining appropriate filter parameters; (ii) a cross-validation approach for selecting appropriate background and foreground seeds; and (iii) a weight-based approach for merging the rough saliency maps. Our solution is easy-to-understand and implement, but without loss of effectiveness. We have validated its competitiveness through widely used benchmark datasets.