Skip to main content

Colour Image Segmentation Using Homogeneity Method and Data Fusion Techniques

Abstract

A novel method of colour image segmentation based on fuzzy homogeneity and data fusion techniques is presented. The general idea of mass function estimation in the Dempster-Shafer evidence theory of the histogram is extended to the homogeneity domain. The fuzzy homogeneity vector is used to determine the fuzzy region in each primitive colour, whereas, the evidence theory is employed to merge different data sources in order to increase the quality of the information and to obtain an optimal segmented image. Segmentation results from the proposed method are validated and the classification accuracy for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results demonstrate the superiority of introducing the fuzzy homogeneity method in evidence theory for image segmentation.

Publisher note

To access the full article, please see PDF.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Salim Ben Chaabane.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Ben Chaabane, S., Sayadi, M., Fnaiech, F. et al. Colour Image Segmentation Using Homogeneity Method and Data Fusion Techniques. EURASIP J. Adv. Signal Process. 2010, 367297 (2009). https://doi.org/10.1155/2010/367297

Download citation

Keywords

  • Information Technology
  • Classification Accuracy
  • General Idea
  • Quantum Information
  • Image Segmentation