Fuzzy Student’s T-Distribution Model Based on Richer Spatial Combination

Abstract

Fuzzy c-means (FCM) algorithms with spatial information have been widely applied in the field of image segmentation. However, most of them suffer from two challenges. One is that the introduction of fixed or adaptive single neighboring information with narrow receptive field limits contextual constraints leading to clutter segmentations. The other is that the incorporation of superpixels with wide receptive field enlarges spatial coherency leading to block effects. To address these challenges, we propose fuzzy STUDENT’S t-distribution model based on richer spatial combination (FRSC) for image segmentation. In this article, we make two significant contributions. The first is that both the narrow and wide receptive fields are integrated into the objective function of FRSC, which is convenient to mine image features and distinguish local difference. The second is that the rich spatial combination under STUDENT’S t-distribution ensures that spatial information is introduced into the updated parameters of FRSC, which is helpful in finding a balance between the noise-immunity and detail-preservation. Experimental results on synthetic and publicly available images further demonstrate that the proposed FRSC addresses successfully the limitations of FCM algorithms with spatial information, and provides better segmentation results than state-of-the-art clustering algorithms.