Image Fuzzy Clustering Based on the Region-Level Markov Random Field Model

The Markov random field (MRF) model serves as one of the most powerful tools to improve the robustness of fuzzy c-means (FCM) clustering. However, the use of a pixel-level MRF makes the clustering deficient to deal with images with macro texture patterns. In order to overcome such a problem, this letter presents a novel method that segments images by combining FCM with the region-level MRF (RMRF) model. In this method, a fuzzy novel energy function is established for the RMRF model and utilized in the process of fuzzy clustering, which plays an important role in describing large-range variations of macro textures. Considering the complexity of image textures, a region-level mean template is also established to enhance the relationships between neighboring regions in terms of spectral and structural information. Experiments are conducted using high-resolution remote sensing images, which demonstrate that the proposed method can improve the segmentation accuracy compared with four state-of-the-art competitors.