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Segmenting common objects that have variations in color, texture and shape is a challenging problem. In this paper, we propose a new model that efficiently segments common objects from multiple images. We first segment each original image into a number of local regions. Then, we construct a digraph based on local region similarities and saliency maps. Finally, we formulate the co-segmentation problem as the shortest path problem, and we use the dynamic programming method to solve the problem. The experimental results demonstrate that the proposed model can efficiently segment the common objects from a group of images with generally lower error rate than many existing and conventional co-segmentation methods.