Hybrid Label Propagation Semi-supervised Intuitionistic Fuzzy Clustering incorporating Symmetric Property for Image Segmentation

When the existing intuitionistic fuzzy clustering algorithms are applied to image segmentation, only the pixel information of the image is considered. Furthermore, due to neglecting the geometric features and regional information of the image, the segmentation result is not ideal. In order to boost the segmentation performance of the intuitionistic fuzzy clustering algorithm, a hybrid label propagation semi-supervised intuitionistic fuzzy clustering incorporating symmetric property for image segmentation algorithm was proposed. First, the symmetry axis of the image was detected to obtain the symmetric property. Second, it used the symmetry characteristics of the image to perform symmetric label propagation of pixels and to improve the intuitionistic fuzzy distance measure between the pixels and cluster centers. Third, a hybrid label propagation semi-supervised strategy was designed to estimate the membership of all pixels. It introduced the estimated membership as the supervised membership into the intuitionistic fuzzy clustering algorithm. Fourth, it constructed a hybrid label propagation semi-supervised intuitionistic fuzzy clustering objective function incorporating symmetric property. Finally, the final segmentation result was gained by the proposed algorithm. Experimental results on two color image libraries demonstrated that the proposed algorithm could segment the target from the complex background completely, and the segmentation performance was superior to the comparison algorithms.