[1]LIU Qingxin,LI Ni,JIA Heming,et al.An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure[J].CAAI Transactions on Intelligent Systems,2024,19(2):381-391.[doi:10.11992/tis.202205018]
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An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 381-391 Column: 学术论文—智能系统 Public date: 2024-03-05
Title:
An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure
Author(s):
LIU Qingxin1;  LI Ni2; 3;  JIA Heming4;  QI Qi1
1. School of Computer Science and Technology, Hainan University, Haikou 570228, China;
2. School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China;
3. Key Laboratory of Data Science and Intelligence Education of Ministry
Keywords:
image processing; multilevel thresholding segmentation; remora optimization algorithm; minimum cross entropy; lens opposite learning; adaptive weight factor; global optimization; remote sensing image
CLC:
TP391.41;TP18
DOI:
10.11992/tis.202205018
Abstract:
To improve the poor segmentation quality of traditional image thresholding segmentation techniques, this study proposes an image multilevel thresholding segmentation method. This method is based on an improved remora optimization algorithm and entropy measure, specifically called the weight lens remora optimization algorithm (WLROA). First, lens opposition-based learning was used to generate the lens opposite solution. This approach bolstered population diversity and improved the algorithm’s ability to overcome local optimal solutions. Furthermore, an adaptive weight factor was introduced to perturb the individuals’ positions appropriately. This modification aimed to improve the algorithm’s exploratory ability. The optimization objective was to minimize cross entropy. To achieve this, WLROA was used to determine the minimum cross entropy and obtain the corresponding thresholds. A selection of images from the Berkeley segmentation data set and remote sensing images were selected to assess the segmentation performance of the proposed algorithm. These results were then compared with those from other methods. The results revealed that, in comparison with other well-known algorithms, WLROA yielded better segmentation results and proved effective in accurately processing complex images.
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