Abstract
Multilevel thresholding is an effective and indispensable technology for image segmentation that has attracted extensive attention in recent years. However, the multilevel thresholding method has some disadvantages, such as a large computational complexity and low segmentation accuracy. Therefore, this paper proposes a whale optimization algorithm (WOA) based on Kapur's entropy method to solve the image segmentation problem. The WOA can effectively balance exploration and exploitation to avoid falling into premature convergence and obtain the global optimal solution. To verify the segmentation performance of the WOA, a series of experiments on underwater images from the experimental pool of Harbin Engineering University are conducted, and the segmentation results are compared with those of the BA, the FPA, MFO, the MSA, PSO and WWO by maximizing the fitness value of Kapur's entropy method. The fitness value, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), execution time and Wilcoxon's rank-sum test are used to evaluate the overall performance of each algorithm. The experimental results reveal that the WOA is superior to the other comparison algorithms and has a higher segmentation accuracy, better segmentation effect and stronger robustness. In addition, the feasibility and efficiency of the WOA are verified.
Highlights
In recent years, unmanned underwater vehicles (UUVs) with vision systems have been widely used to gather image information for analysis and research, in which underwater image segmentation is difficult to accomplish in machine vision
The video processing is regarded as the processing operation of the video frames, and the underwater video can be used as research object of the underwater image
The image segmentation is the basic step of the image processing, the collection of the underwater images information affects the completion of UUV underwater task to a certain extent
Summary
In recent years, unmanned underwater vehicles (UUVs) with vision systems have been widely used to gather image information for analysis and research, in which underwater image segmentation is difficult to accomplish in machine vision. Zhou et al applied the moth swarm algorithm based on Kapur’s entropy method to solve the multi-threshold image segmentation problem, and the proposed method has proved the robustness and effectiveness according to numerical experimental results and image segmentation results [18]. Díaz-Cortés et al tried to solve the multi-level thresholding image segmentation problem using a dragonfly algorithm, and the experimental results indicated that the proposed algorithm obtained better optimization performance [21]. Jia et al applied a modified moth flame algorithm to solve the multilevel thresholding image segmentation problem, and the results indicated that the proposed algorithm found the optimal objective fitness values and achieved the best segmentation effect [30].
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