Iterative local color normalization using fuzzy image clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


The goal of this paper is to introduce a new fuzzy local iterative algorithm that matches local color statistics of a reference image to the distribution of the input image. Reference images are considered to have a desirable color distribution for a specific application. The proposed algorithm consists of three stages: (1) images clustering by fuzzy cmeans, (2) clusters' matching, and (3) color distribution transfer between the matching clusters. First, a color similarity measurement is used to segment image regions in the reference and input images. Second, we match the most similar clusters in order to avoid the appearing of undesirable artifacts due to differences in the color dynamic range. Third, the color characteristics of the reference clusters are transferred to the equivalent clusters in the input image by applying an iterative process. The new image normalization tool has several advantages: it is computationally efficient and it has the potential of increasing substantially the accuracy of segmentation and classification systems based on analysis of color features. Computer simulations indicate that the iterative and gradual color matching procedure is able to standardize the appearance of color images according to a desirable color distribution and reduce the amount of artifacts appearing in the resulting image.

Original languageEnglish (US)
Title of host publicationMobile Multimedia/Image Processing, Security, and Applications 2013
StatePublished - 2013
Externally publishedYes
EventMobile Multimedia/Image Processing, Security, and Applications 2013 - Baltimore, MD, United States
Duration: Apr 29 2013Apr 30 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceMobile Multimedia/Image Processing, Security, and Applications 2013
Country/TerritoryUnited States
CityBaltimore, MD


  • Color normalization
  • Fuzzy c-means
  • Fuzzy clustering
  • Image recoloring
  • Iterative color distribution mapping

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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