The design of robust and efficient cosegmentation algorithms is challenging because of the variety and complexity of the objects and images. In this paper, we propose a new cosegmentation model by incorporating a color reward strategy and an active contour model. A new energy function corresponding to the curve is first generated with two considerations: the foreground similarity between the image pairs and the background consistency in each of the image pair. Furthermore, a new foreground similarity measurement based on the rewarding strategy is proposed. Then, we minimize the energy function value via a mutual procedure which uses dynamic priors to mutually evolve the curves. The proposed method is evaluated on many images from commonly used databases. The experimental results demonstrate that the proposed model can efficiently segment the common objects from the image pairs with generally lower error rate than many existing and conventional cosegmentation methods.