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In this paper, a novel image change detection algorithm based on clustering characteristic of 2-D histogram formed by pixel gray levels and the local average gray levels is proposed. First, the 2-D histogram is segmented into two initial clusters representing change region and unchanged region respectively by using classical segmentation method. Then, the traditional 2-D maximum entropy principle is improved properly to adjust the initial clusters. Finally, changes are detected according to the two relative more accurate clusters that have been adjusted. Theoretical analysis and experimental results show that the proposed algorithm has more accurate detection precision, stronger anti-noise capability and faster computation than traditional 2-D maximum entropy algorithm.