Adaptive color space transform using independent component analysis

Esteban Vera, Sergio Torres

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


In this paper, a novel color space transform is presented. It is an adaptive transform based on the application of independent component analysis to the RGB data of an entire color image. The result is a linear and reversible color space transform that provides three new coordinate axes where the projected data is as much as statistically independent as possible, and therefore highly uncorrelated. Compared to many non-linear color space transforms such as the HSV or CIE-Lab, the proposed one has the advantage of being a linear transform from the RGB color space, much like the XYZ or YIQ. However, its adaptiveness has the drawback of needing an estimate of the transform matrix for each image, which is sometimes computationally expensive for larger images due to the common iterative nature of the independent component analysis implementations. Then, an image subsampling method is also proposed to enhance the novel color space transform speed, efficiency and robustness. The new color space is used for a large set of test color images, and it is compared to traditional color space transforms, where we can clearly visualize its vast potential as a promising tool for segmentation purposes for example.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Image Processing
Subtitle of host publicationAlgorithms and Systems V
StatePublished - 2007
Externally publishedYes
EventImage Processing: Algorithms and Systems V - San Jose, CA, United States
Duration: 29 Jan 200730 Jan 2007

Publication series

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


ConferenceImage Processing: Algorithms and Systems V
Country/TerritoryUnited States
CitySan Jose, CA


  • Color image processing
  • Color space transform
  • Independent component analysis


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