为了使图像分割效果稳定和提高分割算法的普适性，提出了一种利用多线索动态融合进行图像分割的方法，该方法首先将图像中的颜色信息、纹理信息、空间信息、边界信息融合到一个条件随机场模型中，用于图像分割;然后在对特定图像进行分割时，由于该模型能够利用用户在分割前标定的前景和背景信息来建立一种衡量概率分布估计可靠性的标准，并可通过该标准来组合条件随机场中的相关能量项，使它们能够根据图像的内在属性进行动态融合，从而提高了条件随机场图像分割的自适应性。实验结果表明，该方法分割效果稳定、普适性强，对一般的自然图像均能得到较

In order to improve the stability and generality of image segmentation, a new method based on the dynamic multi-cue integration is presented in this paper. It fuses color, texture, spatial and edge information by the construction of a conditional random field for image segmentation. Besides, in the course of interactive image segmentation, the proposed method is able to take full advantage of the user labeled information about the foreground and background, and builds a certain standard to measure the reliability of established probability distribution, with which to assemble the energy terms in the conditional random field. This makes the related energy terms fusing dynamically in accordance with the inner property of the image,and improves the segmentation power of the model. The experimental results demonstrate that the proposed method performs steadily and works well on various natural images.