Object-Based and Semantic Image Segmentation Using MRF

The problem that the Markov random field (MRF) model captures the structural as well as the stochastic textures for remote sensing image segmentation is considered. As the one-point clique, namely, the external field, reflects the priori knowledge of the relative likelihood of the different region types which is often unknown, one would like to consider only two-pairwise clique in the texture. To this end, the MRF model cannot satisfactorily capture the structural component of the texture. In order to capture the structural texture, in this paper, a reference image is used as the external field. This reference image is obtained by Wold model decomposition which produces a purely random texture image and structural texture image from the original image. The structural component depicts the periodicity and directionality characteristics of the texture, while the former describes the stochastic. Furthermore, in order to achieve a good result of segmentation, such as improving smoothness of the texture edge, the proportion between the external and internal fields should be estimated by regarding it as a parameter of the MRF model. Due to periodicity of the structural texture, a useful by-product is that some long-range interaction is also taken into account. In addition, in order to reduce computation, a modified version of parameter estimation method is presented. Experimental results on remote sensing image demonstrating the performance of the algorithm are presented.