Abstract

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

中文翻译：

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基于核距离的直觉模糊c均值聚类算法及其应用

摘要

图像分割在机器视觉，图像识别和成像应用中起着重要作用。基于模糊c均值聚类算法，提出了一种基于核距离的直觉模糊c均值聚类算法。首先，使用模糊补码算子生成隶属度，从而生成直觉模糊集的犹豫度。其次，使用核函数来计算每个点到聚类中心的距离，而不是欧几里得距离。第三，建立了一个包含犹豫度的新目标函数，对目标函数的优化产生了隶属度和聚类中心的新迭代表达式。将提出的KIFCM算法与模糊c均值聚类（FCM）算法进行了比较，核心模糊c均值聚类（KFCM）算法和直觉模糊c均值聚类（IFCM）算法对五个图像进行分割。实验结果验证了我们提出的KIFCM算法的有效性和优越性。