Abstract: In this paper, an image saliency detection algorithm based on region merging is proposed. Our goal is to divide the image into the background region and the salient region directly via a region merging process. To achieve this, different merging strategies are employed in different stages. Firstly, the image is over-segmented into superpixels. In the first stage, only similar neighboring superpixels are allowed to be merged together. Then, in the second stage, occlusions and hole-regions produced in the last step are handled. At last, instead of merging salient regions together, non-salient regions are merged into the background region guided by the region saliency analysis. The final saliency result can be obtained via a weighted average of several saliency region proposals obtained during the region merging process. Our algorithm is tested on the two public datasets and compared with other state-of-the-art algorithms. The experimental results show that our algorithm is effective and efficient. Particularly, for the more challenging dataset ECSSD, the accuracy of our algorithm outperforms other region-based state-of-the-art algorithms.