改进的分层马尔可夫随机场彩色图像分割算法

针对传统的分层马尔可夫随机场（MRF）算法难以描述彩色图像像素值分布等问题，提出一种基于RGB色彩统计分布的分层MRF分割算法。在分层MRF模型的基础上，设定了相关参数并对分割过程进行了公式推导；结合RGB色彩统计分布模型，重写了分层MRF能量函数，利用k-means算法作为预分割算法，实现了算法的无监督分割。相比传统的分层MRF分割模型，该算法充分利用了彩色图像的像素值的信息，可有效地减少颜色分布参数和计算成本，能更准确地描述各分割对象的颜色分布；且该算法不受目标和背景颜色区间分布、目标空间分布的限制，能够很好地描述不同目标和背景。通过大量实验验证了算法的有效性，其在运算速度、分割精度等方面均优于传统MRF算法和模糊C均值（FCM）算法。

Improved hierarchical Markov random field algorithm color image segmentation algorithm

The distribution of color image pixel value is difficult to describe in hierarchical Markov Random Field (MRF) segmentation algorithm, therefore, a hierarchical MRF segmentation algorithm based on RGB color statistic distribution was proposed to solve this problem. The key parameters of the MRF model were set up, and the related formulas were deduced. With the RGB color statistic distribution model, the hierarchical MRF energy function was rewritten, and the k-means algorithm was used as presegmentation method to realize unsupervised segmentation. The proposed algorithm has fewer color distribution parameters and lower computational cost in comparison with traditional MRF segmentation model, which describes color distribution more accurately; and it can describe different targets and background very well without being restricted by target and background color distribution and target spatial distribution. Experimental results prove the effectiveness of the proposed algorithm, which is superior to the MRF algorithm and Fuzzy C-Means (FCM) algorithm in computing speed and segmentation accuracy.