A novel segmentation method of texture image based on multi-scale random field model in wavelet domain

In the paper, a hierarchical Gauss-Markov random field model in wavelet domain is proposed and applied to the texture image segmentation. Multi-scale random field model methods only consider inter-scale casual Markov random field model to describe local statistic information, which are not satisfactory as segmentation results. To address this deficiency, we used Gauss-Markov random field to model, meanwhile take more intra-scale local spatial interaction information into consideration. We use supervised method for training image parameters and multi-objective solving technique to optimize sequential maximum a posteriori estimation. We demonstrate the performance of the proposed method with texture images and noised images. From the visual effect and classification correct ratio, the resulting wavelet-based hierarchical Gauss-Markov random field model is more accurate and robust than the wavelet-based multi-scale random field model method.