Improved Superpixel-Based Fast Fuzzy C-Means Clustering for Image Segmentation

Chong Wu*, Le Zhang, Houwang Zhang, Hong Yan

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

26 Citations (Scopus)

Abstract

Superpixel-based fast fuzzy C-means clustering (SFFCM) is an efficient method for color image segmentation. However, it is sensitive to noise and blur. Its superpixel method called multiscale morphological gradient reconstruction (MMGR) is time consuming. In this paper, we propose an improved SFFCM method (ISFFCM) which replaces the MMGR in SFFCM with fuzzy simple linear iterative clustering (Fuzzy SLIC). Fuzzy SLIC is faster and more robust than MMGR for most types of noise, including salt and pepper noise, Gaussian noise and multiplicative noise. It is also more robust to image blur. In the validation experiments, we tested ISFFCM and SFFCM on the Berkeley benchmark. The experiment results show that our method outperforms SFFCM under noise and blurring environments. 
Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing - Proceedings
PublisherIEEE
Pages1455-1459
ISBN (Electronic)978-1-5386-6249-6
DOIs
Publication statusPublished - Sept 2019
Event26th IEEE International Conference on Image Processing (ICIP 2019) - Taipei International Convention Center (TICC), Taipei, Taiwan, China
Duration: 22 Sept 201925 Sept 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing (ICIP 2019)
Abbreviated titleIEEE ICIP 2019
PlaceTaiwan, China
CityTaipei
Period22/09/1925/09/19

Research Keywords

  • color image segmentation
  • Fuzzy SLIC
  • SFFCM
  • Superpixel

Fingerprint

Dive into the research topics of 'Improved Superpixel-Based Fast Fuzzy C-Means Clustering for Image Segmentation'. Together they form a unique fingerprint.

Cite this