Most existing high-performance co-segmentation algorithms are usually complex due to the way of co-labeling a set of images as well as the common need of fine-tuning few parameters for effective co-segmentation. In this paper, instead of following the conventional way of co-labeling multiple images, we propose to first exploit inter-image information through co-saliency, and then perform single-image segmentation on each individual image. To make the system robust and to avoid heavy dependence on one single saliency extraction method, we propose to apply multiple existing saliency extraction methods on each image to obtain diverse salient maps. Our major contribution lies in the proposed method that fuses the obtained diverse saliency maps by exploiting the inter-image information, which we call saliency co-fusion. Experiments on five benchmark datasets with eight saliency extraction methods show that our saliency co-fusion-based approach achieves competitive performance even without parameter fine-tuning when compared with the state-of-the-art methods.