An improved cuckoo search algorithm for multi-level gray-scale image thresholding

In decades, Yang's cuckoo search algorithm has been widely developed to select the optimal threshold of bi-level image threshoding, but the amount of computation of which increases exponentially with multi-level thresholding. To reduce the computation quantity, the iterative step size is adaptively decided by its fitness values of the current iteration without using the Levy distribution in this study. The modification may cause the solution drops into the local optima during the later period. Therefore, the constant discovery probability p(a) is automatically changed relating to the current and total iterations. And then, to verify segmentation accuracy and efficiency of the proposed method, an adaptive cuckoo search algorithm proposed by Naik and Yang's cuckoo search algorithm are included to test on several gray-scale images. The results show that the proposed algorithm is expert in selecting optimal thresholds for segmenting gray-scale image.