An unsupervised color image segmentation algorithm based on context information

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.