Considering the weakness of the selective search method that needs a large number of windows to localize objects, a novel object localization method based on fusion of visual saliency and superpixels was proposed in this paper. Firstly, the visual saliency map was used to coarsely localize the objects, and then the adjacent superpixels could be merged according to the appearance features of image, starting from the above coarse positions. Furthermore, the method employed a simple background detector to avoid the over-merge. Finally, a greedy algorithm was used to iteratively combine the merged regions and generate the final bounding boxes. The experimental results on Pascal VOC 2007 show that the proposed method leads to a 20% reduction in the number of the bounding boxes on the same detection rate (recall of 0.91) compared to the selective search algorithm, and its overlap rate reaches 0.77. The presented method can keep higher overlap rate and recall scores with fewer windows because of its coarse-to-fine process.