一种基于图像上下文信息的无监督彩色图像分割算法

An Unsupervised Color Image Segmentation Algorithm Based on Context Information

提出一种基于图像上下文信息的彩色图像无监督分割算法.根据传统马尔可夫随机场(MRF)势函数的定义,引入图像邻域内每两像素间亮度欧氏距离及空间位置信息.完善传统马尔可夫随机场模型中的势函数.将分割问题转化为最大后验问题并运用迭代条件模型求解.运用K均值算法在设定的分类数范围内初始化分割,运用最小消息长度准则选择最佳分类数.实现无监督分割.实验中,将合成图像及真实图像用于分割过程并与其它算法比较,证明本文算法更具优势.

An unsupervised color image segmentation method based on image context information is proposed. According to the traditional markov random field （MRF） potential function, the method involves intensity Euclidean distance and spatial position information of pixels in the neighborhood of the image. Therefore, the traditional potential function of MRF segmentation method is improved. The segmentation is transformed into the problem of maximum a posteriori （MAP） which is solved by the iterative conditional model. And K -means is used to initialize the classification in the range of the specified classification numbers. The optimal class number is chosen according to the minimum message length （MML） criterion to complete an unsupervised segmentation. In the experiments, synthetic and real images are employed in segmentation procedure. Compared with other methods, the proposed algorithm is proved to be more effective.