Interval type-2 possibilistic fuzzy clustering noisy image segmentation algorithm with adaptive spatial constraints and local feature weighting & clustering weighting

Abstract

The interval type-2 possibilistic fuzzy C-means (IT2PFCM) algorithm is a popular data clustering and image segmentation method. However, the algorithm fails to consider the spatial information of the image and has limited accuracy for noisy images. The other problem of the IT2PFCM algorithm assumes that all features are equally important in the segmentation process and perform less well as it segments color images. In addition, the IT2PFCM algorithm produces overlapping clusters and even misses some clusters if the groups contained in the data are relatively close to each other. We propose an improved IT2PFCM algorithm for noisy image segmentation to solve the problems. Firstly, an adaptive spatial constraint method is proposed to decrease the algorithm's sensitivity to noise. Secondly, we utilize an automatic local feature weighting and clustering weighting scheme to better segment color images and eliminate overlapping clusters. Finally, an objective function with adaptive spatial constraint, local feature weighting, and clustering weighting is constructed for image segmentation. Extensive experiments on synthetic and natural images show that the proposed algorithm outperforms the state-of-the-art algorithms. The convergence proof of the proposed algorithm is also provided.