Co-saliency detection, an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of images. This letter proposes a hierarchical segmentation based co-saliency model. On the basis of fine segmentation, regional histograms are used to measure regional similarities between region pairs in the image set, and regional contrasts within each image are exploited to evaluate the intra-saliency of each region. On the basis of coarse segmentation, an object prior for each region is measured based on the connectivity with image borders. Finally, the global similarity of each region is derived based on regional similarity measures, and then effectively integrated with intra-saliency map and object prior map to generate the co-saliency map for each image. Experimental results on two benchmark datasets demonstrate the better co-saliency detection performance of the proposed model compared to the state-of-the-art co-saliency models.