We propose a simple but effective approach to detect salient objects by exploring both patch-level and object-level cues under the framework of absorbing Markov chain. Saliency detection is carried out in a two-stage scheme. In the first stage, we conduct random walk on absorbing Markov chain with coarsely selected background seeds in the boundary. The result is integrated with a objectness map which is generated by finding potential object candidates to boost the saliency detection for object completeness. And in the second stage, we use the refined background seeds computed by the first stage as absorbing nodes for the absorbing Markov chain to obtain the final saliency map. Experimental results on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 8 state-of-the-art methods in terms of five performance criterions.