G-Image Segmentation: Similarity-Preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space

Cong Wang, Witold Pedrycz, Zhiwu Li, Mengchu Zhou, Shuzhi Sam Ge

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

G-images refer to image data defined on irregular graph domains. This article elaborates on a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on partition matrix is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than the Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art segmentation algorithms. Moreover, it requires less computation than most of them.

Original languageEnglish (US)
Pages (from-to)3887-3898
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Volume29
Issue number12
DOIs
StatePublished - Dec 1 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Keywords

  • Fuzzy C -Means (FCM)
  • G-image segmentation
  • similarity-preserving
  • spatial information
  • wavelet space

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