Image thresholding based on Pareto multiobjective optimization

Authors:
A. Nakib;H. Oulhadj;P. Siarry
Affiliations:
Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi, E. A. 3956) Université de Paris 12, 61 avenue du Général de Gaulle, 94010 Créteil, France;Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi, E. A. 3956) Université de Paris 12, 61 avenue du Général de Gaulle, 94010 Créteil, France;Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi, E. A. 3956) Université de Paris 12, 61 avenue du Général de Gaulle, 94010 Créteil, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 0
Cited 3

Review Article: Multi-objective nature-inspired clustering and classification techniques for image segmentation

Applied Soft Computing
Simultaneous image color correction and enhancement using particle swarm optimization

Engineering Applications of Artificial Intelligence
Multi-objective image segmentation with an interactive evolutionary computation approach

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new image thresholding method based on multiobjective optimization following the Pareto approach is presented. This method allows to optimize several segmentation criteria simultaneously, in order to improve the quality of the segmentation. To obtain the Pareto front and then the optimal Pareto solution, we adapted the evolutionary algorithm NSGA-II (Deb et al., 2002). The final solution or Pareto solution corresponds to that allowing a compromise between the different segmentation criteria, without favouring any one. The proposed method was evaluated on various types of images. The obtained results show the robustness of the method, and its non dependence towards the kind of the image to be segmented.