Multi-resolution Markov Random Field Model with Variable Potentials in Wavelet Domain for Texture Image Segmentation
The traditional multi-resolution Markov random field (MRMRF) model uses two-component Markov random field model on each resolution, and requires training data to estimate the necessary model parameters, which is unsuitable for unsupervised image segmentation. Under this circumstance, a new multi-resolution Markov random field model with variable potential for unsupervised texture image segmentation is presented. The new model solves this problem by introducing a variable potential function for multi-level logistic distribution (MLL) model on each scale. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.
image segmentation multiresolution markov random field variable potential
LI Qingsheng LIU Guoying
School of Computer and Information Engineering Anyang Normal University Anyang Henan, China
国际会议
太原
英文
342-346
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）