A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy.

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search methods are costly in determining optimal thresholds and the conventional remora optimization algorithm (ROA) is prone to the premature convergence. This paper presents a chimp-inspired remora optimization algorithm (HCROA) to search optimal threshold levels, and the cross-entropy is employed as the objective function. In HCROA, the particles' position are adjusted by the Chimp Optimization Algorithm (ChOA) because of its good exploitation ability and sufficient diversity. With this change, HCROA achieves both the intra-group diversity intelligence and a suitable balance between exploration and exploitation. To validate its performance, a series of experiments are performed. First, we test the HCROA's segmentation accuracy by a set of natural gray-scale images with different thresholds. Second, HCROA is implemented for noisy image segmentation to evaluate its robustness. Several reference-based measurements including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Feature Similarity (FSIM), Quality Index based on Local Variance (QILV), Haar wavelet-based Perceptual Similarity Index (HPSI), Wilcoxon test, and CPU time have been considered for evaluating the proposed method. Additionally, eight well-known predecessors are injected for parallel comparison. The comparison results prove that the suggested method outperforms the existing approaches in terms of accuracy, convergence speed, noise robustness, and efficiency. [ABSTRACT FROM AUTHOR]