Image Information Driven Suppression Rough Fuzzy Clustering Segmentation Algorithm

Rough fuzzy clustering methods need setting the threshold manually to determine the upper and lower approximation for being sensitive to the noise. To reduce human intervention, this paper proposed a suppression rough fuzzy clustering segmentation algorithm driven by image information realizing the deep application of rough fuzzy clustering. The method designed an adaptive threshold strategy based on super-pixel region information to effectively determine the upper and lower approximation. The image spatial information was introduced into rough fuzzy clustering to construct the objective function for overcoming the sensitivity of the method with noise. Besides, the idea of suppression study in fuzzy clustering was introduced for correcting the fuzzy membership degree in rough approximation set, which realized the deep fusion of rough and fuzzy ideas. This algorithm is a rough fuzzy clustering image segmentation algorithm with more hybrid intelligent mechanism. The experimental results show the effectiveness of this algorithm.