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

模式识别(心理学) 聚类分析 马氏距离 人工智能 模糊聚类 核(代数) 维数之咒 图像分割 数学 计算机科学 离群值 火焰团簇 相关聚类 CURE数据聚类算法 数据挖掘 分割 组合数学
作者
Haiyan Yu,Shuang Xie,Jiulun Fan,Rong Lan,Bo Lei
出处
期刊:IEEE Transactions on Fuzzy Systems [Institute of Electrical and Electronics Engineers]
卷期号:32 (8): 4595-4609 被引量:4
标识
DOI:10.1109/tfuzz.2024.3405497
摘要

The Possibilistic c-means clustering (PCM) 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 paper proposes a possibilistic c-means 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 paper 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.
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