Image co-segmentation is the problem of extracting common objects from multiple images and it is a very challenging task. In this paper we try to address the co-segmentation problem by embedding image saliency into active contour model. Active contour is a very famous and effective image segmentation method but performs poor results if applied directly to co-segmentation. Therefore, we can introduce additional information to improve the segmentation results, such as saliency which can show the region of interest. In order to optimize the model, we propose an efficient level-set optimization method based on super-pixels, hierarchical computation and convergence judgment. We evaluated the proposed method on iCoseg and MSRC datasets. Compared with other methods, our method yielded better results and demonstrated the significance of using image saliency in active contour.
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