Skip to main content

Optimization-Based Image Segmentation by Genetic Algorithms

Abstract

Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.

Publisher note

To access the full article, please see PDF.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H Laurent.

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

Chabrier, S., Rosenberger, C., Emile, B. et al. Optimization-Based Image Segmentation by Genetic Algorithms. J Image Video Proc 2008, 842029 (2008). https://doi.org/10.1155/2008/842029

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2008/842029

Keywords

  • Genetic Algorithm
  • Evaluation Criterion
  • Ground Truth
  • Image Segmentation
  • Good Combination