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Grey prediction evolution algorithm with a dominator guidance strategy for solving multi-level image thresholding
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AI
DOI：10.1016/j.asoc.2025.112947.png)
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Abstract
En 中文
Multi-level thresholding (MLT) stands as a pivotal method for extracting target information from images. Meta-heuristic algorithms provide an efficient way to implement MLT and retains more research space for accuracy optimization of high-dimensional multi-level thresholding (HDMLT) of images than they do for low-dimensional multi-level thresholding (LDMIT). In order to improve the algorithmic accuracy in solving the high-dimensional problems, a grey prediction evolution algorithm with a dominator guidance strategy (GPEdg) is proposed in this paper. GPEdg employs Otsu's method as its objective function to find the best threshold configuration. The novel operator in the algorithm, i.e., a dominator guidance (dg) strategy, uses a linear combination of three difference vectors to guide the top 50% individuals of populations to learn from the top 20% of them. An efficient balance of search abilities suitable for solving HDMLT problems is expected to be achieved by injecting the local search capability of the dg strategy into GPE's powerful global search capability. Furthermore, a thresholding morphological profile based method (TMP) leverages the thresholding results generated by GPEdg to train a support vector machine (SVM) for hyperspectral image classification. Numerical experiments are conducted for the newly proposed algorithm and five state-of-the-art algorithms on three image datasets to compare the performance in six metrics, i.e., peak signal-to-noise ratio, structural similarity index, features similarity index, objective function value, stability and time consumption. Overall accuracy and average accuracy are tested on two commonly used hyperspectral image data. The results show that GPEdg exhibits outstanding thresholding performance while TMP enhances the classification accuracy of these images. If this paper is accepted, Matlab_codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE
Keywords:
Multi-level image thresholding
Evolution algorithm
Otsu's method
Hyperspectral image classification