Solving Multi-level Image Thresholding Problem—An Analysis with Cuckoo Search Algorithm

In recent years, heuristic algorithms are extensively employed to offer optimal solutions for a class of engineering optimization problems. In this paper, Otsu based bi-level and multi-level image segmentation problem is addressed using Cuckoo Search (CS) algorithm. Optimal thresholds for the gray scale images are attained by analyzing histogram of the image. Maximization of Otsu’s between class variance function is chosen as the objective function. In the proposed work, CS algorithm with various search methodologies, such as Levy Flight (LF), Brownian Distribution (BD), and Chaotic search are analyzed. The proposed work is demonstrated by considering five grey scale benchmark (512 × 512) images. The performance assessment between CS algorithms are carried using established image parameters such as objective function, Root Mean Squared Error (RMSE), Peak to Signal Ratio (PSNR), and Structural Similarity Index Matrix (SSIM). The result shows that BD and chaotic CS provide better objective function, PSNR and SSIM, whereas LF based CS offers faster convergence.