An Enhanced Spatial Intuitionistic Fuzzy C-means Clustering for Image Segmentation

被引：17

作者：

Arora, Jyoti ^{[1
]}

Tushir, Meena ^{[2
]}

机构：

[1] Maharaja Surajmal Inst Technol, GGSIPU, Dept Informat Technol, New Delhi, India

[2] Maharaja Surajmal Inst Technol, GGSIPU, Dept Elect & Elect Engn, New Delhi, India

来源：

INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE | 2020年 / 167卷

关键词：

Clustering; Image Segmentation; Fuzzy C-means; Intuitionistic Fuzzy C-means; INFORMATION;

D O I：

10.1016/j.procs.2020.03.331

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Intuitionistic based Fuzzy clustering is a popular method in the field of image segmentation. The widely used Intuitionistic Fuzzy C-means (IFCM) based image segmentation is sensitive to noise since it uses only distance criterion in the feature space to segment the images. To overcome this, an enhanced spatial intuitionistic fuzzy c-means clustering algorithm is proposed that uses (i) an intuitionistic fuzzification of image to simplify the representation of the image (ii) an improved method to calculate the hesitation degree in the images. (iii) the spatial property of an image in order to make segmentation more robust and effective. The performance of the proposed method is evaluated for synthetic and real images. The result indicates the effectiveness of the proposed methodology over existing methodologies. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页码：646 / 655

页数：10

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