关键词: 探路者算法, 多阈值, 图像分割, Tent映射, 自适应t分布

Abstract: There are some problems in multilevel threshold image segmentation, such as large amount of computation and long running time. A new multilevel threshold image segmentation method named improved pathfinder algorithm (IPFA) is proposed using Tent map and adaptive t-distribution strategy on the standard of pathfinder algorithm (PFA). This method uses Kapur’s entropy as the objective function to search the best segmentation threshold. In order to verify the effectiveness of the algorithm, the convergence accuracy and speed of IPFA are tested by benchmark functions at first. Then IPFA-Kapur is applied to multilevel threshold image segmentation and compared with standard PFA, moth-flame optimization (MFO), gray wolf optimization (GWO) and particle swarm optimization (PSO). Experimental results show that the proposed algorithm has faster convergence speed and higher segmentation accuracy, and has better segmentation effect than other comparison algorithms, and the peak signal to noise ratio (PSNR) and structural similarity (SSIM) have better performance, which can effectively solve the problem of multilevel threshold image segmentation.

Key words: pathfinder algorithm, multilevel threshold, image segmentation, Tent map, adaptive t-distribution