A multi-level image thresholding approach using Otsu based on the improved invasive weed optimization algorithm

Multi-level thresholding is one of the most popular techniques in image segmentation. However, selecting the optimal thresholds with high accuracy and efficiency is still challenging. In this paper, a novel multi-level thresholding method using between-class variance (Otsu) based on an improved invasive weed optimization algorithm (FIWO) is proposed. In the FIWO algorithm, the forking technique of the lightning search algorithm is introduced to guarantee the quality of the initial population and to enhance the exploration of the algorithm. In addition, the current best solution swing operation is used to obtain the optimal thresholds with a fast convergence rate. Comparative experiments are carried out to test the performance of FIWO. The results show that the proposed FIWO algorithm is able to achieve better segmented images with fewer iterations than those of the simulated annealing algorithm, gravitational search algorithm, whale optimization algorithm and traditional invasive weed optimization algorithm.