An improved cuckoo search algorithm for multilevel color image thresholding based on modified fuzzy entropy

Multilevel thresholding is a significant technology for image segmentation. Traditional exhaustive search method to solve the optimal multilevel thresholds is time consuming, color images contain more information are even worse. To address this issue, an improved cuckoo search algorithm (ICS) is proposed for color image segmentation in this paper, and a modified fuzzy entropy is used as its objective function. In ICS, two modifications are adopted to improve the standard cuckoo search algorithm. First, an adaptive control parameter mechanism is utilized to enhance the performance of exploration. Second, a hybrid search strategy is used to boost the local search efficiency. To fully demonstrate the superior performance of the ICS, the experiments are conducted on a series of color benchmark test images, and a total of six optimization algorithms are compared with the proposed algorithm. The experimental results show that the presented ICS algorithm outperforms all the other algorithms in term of objective function value, PSNR, FSIM, convergence speed and parametric statistical test. Compared to other algorithms, the ICS algorithm is an effective method for multilevel color image thresholding segmentation.

Automated summary

The improved cuckoo search algorithm (ICS) proposed in this paper for color image segmentation utilizes a modified fuzzy entropy as its objective function, with adaptive control parameter mechanism and hybrid search strategy for enhanced performance. Experimental results demonstrate that the ICS algorithm outperforms others in terms of objective function value, PSNR, FSIM, convergence speed, and statistical tests.