A saliency detection method based on the PageRank algorithm and local spline regression (LSR) is proposed. Unlike the principles of most existing bottom-up methods, in the proposed method, saliency detection is considered a label propagation and regression problem. The input image is represented as two-scale graphs with homogeneous superpixels. Multiple color features and spatial information are effectively captured to define the relevance of each node to its surroundings. PageRank is used to assign the saliency value to each region depending on the similarity of the image elements with boundary cues. Furthermore, to comprehensively consider both foreground and background cues, a robust classifier based on LSR is employed to highlight more complete foreground regions. The integrated and refined pixel-level saliency map provides a significant performance boost in the final result. Extensive experiments on three large public datasets demonstrate that the proposed algorithm consistently achieves superior performance compared with state-of-the-art saliency models.