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Though effective in the segmentation, conventional multilevel thresholdingmethods are computationally expensive as exhaustive search are used for optimalthresholds to optimize the objective functions. To overcome this problem,population-based metaheuristic algorithms are widely used to improve thesearching capacity. In this paper, we improve a popular metaheuristic calledcuckoo search using a ring topology based fully informed strategy. In thisstrategy, each individual in the population learns from its neighborhoods toimprove the cooperation of the population and the learning efficiency. Bestsolution or best fitness value can be obtained from the initial randomthreshold values, whose quality is evaluated by the correlation function.Experimental results have been examined on various numbers of thresholds. Theresults demonstrate that the proposed algorithm is more accurate and efficientthan other four popular methods.

Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm