A twostage guided cosaliency detection model based on interimage saliency propagation and intraimage manifold ranking is proposed to fully exploit the saliency bootstrap propagation mechanism of single image, and improve the accuracy of the cosaliency detection algorithm. For any pair of images in a group image containing N images, the first intersaliency propagation stage utilizes the similarity between a pair of images to discover common properties of the images and get N-1 initial cosaliency maps with the help of a single image saliency map. In order to effectively suppress the background disturbance, the efficient manifold ranking（EMR）algorithm is used to calculate the ranking scores of each initial cosaliency maps in the second stage. The ranking scores are then directly assigned to all pixels as their new saliency values. Finally, an integration algorithm is proposed in the Bayesian framework to get the final cosaliency map. Based on iCoseg and MSRC image databases, the experimental results show that the proposed algorithm is superior to the five existing cosaliency detection algorithms uniformly in Fmeasure and the area under ROC curve (AUC) value. The algorithm is further validated by the real context experiment from the general practicability principle.