Optimal multilevel thresholding using bacterial foraging algorithm

The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive extending to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this drawback, a bacterial foraging (BF) algorithm based multilevel thresholding is presented in this paper. The BF algorithm is used to find the optimal threshold values for maximizing the Kapur’s and Otsu’s objective functions. The feasibility of the proposed BF technique has been tested on ten standard test images and benchmarked with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). Experimental results of both qualitative and quantitative comparative studies for several existing methods illustrate the effectiveness and robustness of the proposed algorithm.