An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation

In this paper an Adaptive Bacterial Foraging is proposed for fuzzy entropy optimization when it is applied to the segmentation of gray images. The proposed algorithm represents the improved version of classical bacterial foraging algorithm which is a newly developed stochastic optimization tool. This optimization technique is applied for optimization of the fitness function which is fuzzy entropy. Classical bacterial foraging algorithm is improved by adaptively selecting the exploitation and exploration state in chemotaxis of E.coli. bacteria. The newly developed algorithm is applied on benchmark gray images and proved to be suitable for thresholding based image segmentation.