Abstract:

The existing image segmentation algorithms based on Markov random field are prone to over segmentation and the segmentation results are not ideal. This paper presents an improved image segmentation algorithm based on Markov random field and region merging. First, the algorithm uses the image segmentation algorithm based on the theory of Markov random field and Gaussian mixture model to get the initial segmentation results; second, the region distance between each region is given by using the adjacent relationship, color relationship and boundary condition of each region; finally, the initial segmentation is performed according to the distance between regions and the change rate of color divergence after region merging. The final image segmentation results are output by region merging. In this paper, Berkeley standard image library is used for experimental simulation, and the Dice and Jaccard coefficients are used as the evaluation index of this paper. The experimental simulation shows that the proposed algorithm has better segmentation effect than the existing algorithm based on MRF theory.

Key words: Image segmentation, Markov random field, Gaussian mixture model, Region merging

摘要：

针对现有基于马尔科夫随机场的图像分割算法容易出现过分割、分割结果不理想等问题，提出了一种基于马尔科夫随机场与区域合并的图像分割改进算法。该算法首先基于马尔科夫随机场与高斯混合模型理论的图像分割算法得到初始分割结果；然后利用各个区域间的相邻关系、颜色关系以及边界情况等信息，给出各个区域间的距离；最后按照区域间的距离与区域合并前后的颜色散度变化率对初始分割结果进行区域合并，输出最终的分割结果。使用伯克利标准图像库进行实验仿真，采用Dice系数和Jaccard系数作为评价指标。仿真结果表明，相比于现有基于MRF理论的算法，本文算法具有更好的分割效果。

关键词: 图像分割, 马尔科夫随机场, 高斯混合模型, 区域合并