基于快速收敛LBP算法的图像分割

Image segmentation based on fast converging loopy belief propagation algorithm

针对循环信度传播(LBP)算法计算量大及误分率高的问题,提出了一种基于快速局部区域收敛的LBP算法的图像分割方法。首先建立局部区域Gibbs能量模型,然后采用局部收敛的LBP算法对区域消息进行传播。为了提高LBP算法的运行速度,提出了一个有效的加速技术。最后,使用局部区域能量的最大后验准则(MAP)得到分割结果。实验结果表明,提出的算法得到了较好的分割结果,特别是在噪声和纹理区域,分割效果明显提升,同时具有较快的速度。

Large-scale computing and high mis-classification rate are two disadvantages of Loopy Belief Propagation（LBP） algorithm for image segmentation.A fast image segmentation method based on LBP algorithm was proposed.At first,a local region Gibbs energy model was built up.Then the region messages were propagated by LBP algorithm.In order to improve the running speed for LBP algorithm,an efficient speedup technique was used.At last,the segmentation result was obtained by the Maximum A Posterior（MAP） criterion of local region Gibbs energy.The experimental results show that the proposed algorithm not only obtains more accurate segmentation results,especially to noise or texture image,but also implements more fast.