Digital Image Multi-threshold Segmentation Method Based on Bayesian Learning
The classical Otsu segmentation algorithm has high accuracy and strong fitness,but it has certain appli-cation limitations in practical use because of its poor segmentation stability and low computational efficiency.In order to improve the stability and computational efficiency of Otsu image segmentation,this paper proposes a Bayesian net-work structure learning algorithm to optimize the search nodes and improve the overall computational efficiency.First-ly,through the independence test in the BN layer,the initial population BN_0 was constructed,and the GR algorithm was used to continuously modify the evolutionary direction;then the MSWT tree network was constructed to solve the problem of low convergence rate caused by multiple father nodes when the population expands;then the ACO algo-rithm was used to plan the individual transition tabu list,and the optimal threshold planning was completed by combi-ning the individual transition probability;finally an OEA-Otsu digital image multi-threshold segmentation model was constructed,and the image multi-threshold segmentation was carried out by analyzing the fitness function and using the optimal Gbest structure planning.The simulation results show that compared with other baseline optimization algo-rithms,the FSIM index of the image multi-threshold segmentation model optimized by OEA is improved by 5.91%,the SSIM index is improved by at least 2.33%,and the PSNR index analysis is increased by 12.76%,10.74%and 12.48%respectively in the experimental data.That is to say,the proposed OEA-Otsu model effectively improves the stability and computational efficiency of image segmentation.
NETL
NSTL
万方数据
