Adaptive region constrained FCM algorithm for image segmentation with hierarchical superpixels

Spatially fuzzy c-means (FCM) clustering has been successfully applied in the field of image segmentation. However, due to the existence of noise and intensity inhomogeneity in images, most of the spatial constraint model fail to resolve misclassification problem. To further improve the segmentation accuracy, a robust spatially constrained FCM-based image segmentation method with hierarchical region information is proposed in this paper. First, two-level superpixles of the input image are generated by two classical segmentation methods, and the first level superpixels instead of the pixels are as input of FCM. Second, by considering the use of the spatial constraints with high-level superpixels, a novel membership function of the first-level superpixels is designed to overcome the impact of noise in the image and accelerate the convergence of clustering process. Through using superpixels instead of pixels and incorporating superpixel information into the spatial constraints, the proposed method can achieve highly consistent segmentation results. Experimental results on the Berkeley image database demonstrate the good performance of the proposed method.