Optimization Based Image Segmentation by Genetic Algorithms

Sébastien Chabrier (1) , Christophe Rosenberger (2) , Bruno Emile (3) , Hélène Laurent (3)

1 JETO - Terre-Océan
2 Equipe SAFE - Laboratoire GREYC - UMR6072
3 LVR - Laboratoire Vision et Robotique

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 grey-levels or multi-components 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 multi-components images.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Sébastien Chabrier, Christophe Rosenberger, Bruno Emile, Hélène Laurent. Optimization Based Image Segmentation by Genetic Algorithms. EURASIP Journal on Image and Video Processing, 2008, pp.1-23. ⟨hal-00255987⟩