Mahalanobis-Kernel Distance-Based Suppressed Possibilistic C-Means Clustering Algorithm for Imbalanced Image Segmentation

The possibilistic c-means (PCM) clustering is an important unsupervised pattern recognition method. However, it is still faced with huge challenges in clustering multidimensional data with multiple characteristics, such as imbalanced sample sizes, imbalanced feature components, noise and outlier corruption, and the sparse distribution of small targets in the feature space caused by the “curse of dimensionality.” In view of this, this article proposes a PCM clustering algorithm based on the Mahalanobis-Kernel Distance and the suppressed competitive learning strategy. To begin with, the Mahalanobis-Kernel Distance combined with the absolute attribute of possibilistic memberships is proposed to enhance the intra-class compactness of small targets with sparse distribution and feature imbalance. In addition, to overcome the inherent coincident clustering problem caused by possibilistic memberships, the “suppressed competitive learning” mechanism based on the Mahalanobis-Kernel distance is designed to generate cluster cores and correct memberships of objects located within the cluster cores, thus guiding purposefully the clustering process. Furthermore, spatial information is introduced by the membership filtering scheme to improve the segmentation effect of color images with small targets and noise injection. Experimental results show that the algorithm in this article can achieve better clustering and segmentation performance than several state-of-the-art fuzzy clustering methods for color images with imbalanced sizes and features and noise injection.