Image Thresholding Based on Bacterial Foraging and Pareto Multiobjective Optimization

Social foraging behavior of Escherichia coli bacteriahas recently been explored to develop a novel algorithm fordistributed optimization and control. This paper exploits themetaphor of natural foraging of bacteria in the context of imagesegmentation. We adapt the bacteria chemotaxis multi-objectiveoptimization algorithm to optimize simultaneously twosegmentation criteria (Between-class variance criterion andentropy criterion) to improve the quality of the segmentation.The proposed method was evaluated on various types of images.The obtained results show the robustness of the method, and itsnon dependence towards the kind of the image to be segmented.