Recoloring textile fabric images based on improved fuzzy clustering

Zhe Zou, Hui Liang Shen, Xin Du, Sijie Shao, John Haozhong Xin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


This article proposes a new recoloring method for textile fabric images based on improved fuzzy local information c-means (FLICM) clustering. In the clustering algorithm, the fuzzy factor was modified so that it can produce reliable segmentation in areas with rich details. With the obtained cluster labels and pixel-wise memberships, the color of each pixel is modeled as the linear combination of the two most dominant colors. The recoloring process was then conducted by replacing the specified dominant color with user-provided target colors. Experimental results showed that the proposed method can produce natural and faithful color appearance on both printed and yarn-dyed fabric images, and outperforms the state-of-the-art. Col Res Appl, 42, 115–123, 2017.
Original languageEnglish
Pages (from-to)115-123
Number of pages9
JournalColor Research and Application
Issue number1
Publication statusPublished - 1 Feb 2017


  • color theme design
  • coloration
  • fuzzy clustering
  • image segmentation
  • recoloring
  • textile fabric image

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • General Chemistry
  • General Chemical Engineering


Dive into the research topics of 'Recoloring textile fabric images based on improved fuzzy clustering'. Together they form a unique fingerprint.

Cite this