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基于Logistic回归麻雀算法的图像分割

陈刚 林东 陈飞 陈祥宇

陈刚,林东,陈飞,等. 基于Logistic回归麻雀算法的图像分割[J]. 北京航空航天大学学报,2023,49(3):636-646 doi: 10.13700/j.bh.1001-5965.2021.0268
引用本文: 陈刚,林东,陈飞,等. 基于Logistic回归麻雀算法的图像分割[J]. 北京航空航天大学学报,2023,49(3):636-646 doi: 10.13700/j.bh.1001-5965.2021.0268
CHEN G,LIN D,CHEN F,et al. Image segmentation based on Logistic regression sparrow algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):636-646 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0268
Citation: CHEN G,LIN D,CHEN F,et al. Image segmentation based on Logistic regression sparrow algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):636-646 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0268

基于Logistic回归麻雀算法的图像分割

doi: 10.13700/j.bh.1001-5965.2021.0268
详细信息
    通讯作者:

    E-mail:lindong@fzu.edu.cn

  • 中图分类号: TP391.41

Image segmentation based on Logistic regression sparrow algorithm

More Information
  • 摘要:

    针对麻雀搜索算法后期种群多样性减少、易陷入局部最优解等问题,提出一种新的改进麻雀搜索算法。所提算法先引入小孔成像反向学习策略对发现者的位置进行更新,提升寻优位置的多样性;其次受Logistic模型的启发,提出一种新的自适应因子对安全阈值进行动态控制,平衡所提算法的全局搜索与局部开发的能力。通过与其他算法在6个基准函数上进行仿真对比,结果表明:所提算法的收敛精度与速度均优于其他算法。在工程应用上,用所提算法优化K-means聚类算法进行图像分割,峰值信噪比(PSNR)、结构相似性(SSIM)及特征相似性(FSIM)3种度量指标验证了其良好的分割性能。

     

  • 图 1  小孔成像反向学习原理

    Figure 1.  Principle of reverse learning for small hole imaging

    图 2  不同参数下对应的函数曲线

    Figure 2.  Curves of functions corresponding to different parameters

    图 3  基于MSSA的K-means图像分割

    Figure 3.  K-means image segmentation based on MSSA

    图 4  算法的收敛曲线对比

    Figure 4.  Comparison of convergence curves of algorithms

    图 5  分割结果对比

    Figure 5.  Comparison of segmentation results

    表  1  测试函数

    Table  1.   Test functions

    编号函数名函数表达式取值区间最小值
    F1Zakharov$f(x) = \displaystyle\sum\limits_{i = 1}^d {x_i^2 + { {\left(\displaystyle\sum\limits_{i = 1}^d {0.5i{x_i} } \right)}^2} + { {\left(\displaystyle\sum\limits_{i = 1}^d {0.5i{x_i} } \right)}^4} }$[−5,10]0
    F2Schwefel
    2.22
    $f(x) = \displaystyle\sum\limits_{i = 1}^d {\left| { {x_i} } \right|} + \prod\limits_{i = 1}^d {\left| { {x_i} } \right|}$[−10,10]0
    F3Rotated Hyper-
    Ellipsoid
    $f(x) = \displaystyle\sum\limits_{i = 1}^d { { {\left(\displaystyle\sum\limits_{j = 1}^i { {x_j} } \right)}^2} }$[−100,100]0
    F4Rastrigin$f(x) = \displaystyle\sum\limits_{i = 1}^d {\left[x_i^2 - 10\cos \;(2\text{π} {x_i}) + 10\right]}$[−5.12,5.12]0
    F5Schaffer N.2$f({x_1},{x_2}) = 0.5 + \dfrac{ { { {\sin }^2}(x_1^2 - x_2^2) - 0.5} }{ { { {[1 + 0.001(x_1^2 + x_2^2)]}^2} } }$[−100,100]0
    F6Drop-
    Wave
    $f({x_1},{x_2}) = - \dfrac{ {1 + \cos \;(12\sqrt {x_1^2 + x_2^2} )} }{ {0.5(x_1^2 + x_2^2) + 2} }$[−5.12,5.12]−1
    下载: 导出CSV

    表  2  算法参数设置

    Table  2.   Parameter settings for algorithms

    算法参数设置
    PSO$ {w_{\max }} = 0.9,{w_{\min }} = 0.2,{c_1} = 2,{c_2} = 2 $
    SCA$a = 2,{r_2} \in [0,2\text{π} ],{r_3} \in [ - 2,2],{r_4} \in [0,1]$
    ABCL = round (0.6dP), a=1
    SSA$ {\rm{PD}} = 35,{\rm{ST}} = 0.6,{\rm{SD}} = 70 $
    ISSA${\rm{PD} } = 35,{\rm{ST}} = 0.6,{\rm{SD} } = 70$
    MSSA${\rm{PD} } = 35,{\rm{ST} } = 0.6\left(\dfrac{1}{ {1 + { {\rm{e} }^{t/10 - 20} } } }\right),{\rm{SD} } = 70$
    下载: 导出CSV

    表  3  算法性能对比

    Table  3.   Performance comparison of algorithms

    函数维度PSOSCAABCSSAISSAMSSA
    均值标准差均值标准差均值标准
    均值标准差均值标准差均值标准差
    F1302.52×1029.35×104.19×101.40×101.34×1034.47×1024.23×10−402.32×10−395.36×1041.80×1035.07×10−462.73×10−45
    F2301.29×102.23×106.22×10−15.48×10−18.58×101.58×101.83×10−359.69×10−351.32×10−51.21×10−52.71×10−381.47×10−37
    F3306.11×1022.14×1021.18×1045.52×1036.32×1041.03×1046.68×10−463.66×10−452.97×10−47.07×10−49.46×10−654.81×10−64
    F4302.17×1022.42×108.13×103.61×102.60×1021.27×10001.02×10−72.15×10−700
    F523.36×10−11.72×10−1003.61×10−87.15×10−8000000
    F62−0.881.76×10−1−10−0.991.57×10−5−10−10−10
    下载: 导出CSV

    表  4  时间复杂度对比

    Table  4.   Comparison of time complexity

    算法初始化种
    群阶段
    发现者位置
    更新阶段
    跟随者位置
    更新阶段
    侦察预警
    机制阶段
    全局最优位置
    更新阶段
    SSAO(d+f(d))O(d)O(d)O(d)O(d)
    ISSAO(d+f(d))O(d)O(d)O(d)O(d)
    MSSAO(d+f(d))O(d)O(d)O(d)O(d)
    下载: 导出CSV

    表  5  实验1的分割结果评估

    Table  5.   Evaluation of segmentation results for experiment 1

    算法PSNRSSIMFSIM
    K-means4.596700.027190.64977
    PSO37.519200.568160.94510
    SSA38.362900.618930.92049
    ISSA39.625700.716130.93937
    MSSA39.625700.716130.93937
    下载: 导出CSV

    表  6  实验 2的分割结果评估

    Table  6.   Evaluation of segmentation results for experiment 2

    算法PSNRSSIMFSIM
    K-means4.012600.025740.61662
    PSO36.238700.462390.93554
    SSA41.532600.750250.94916
    ISSA39.730700.657580.94255
    MSSA41.553400.751000.94976
    下载: 导出CSV

    表  7  实验3的分割结果评估

    Table  7.   Evaluation of segmentation results for experiment 3

    算法PSNRSSIMFSIM
    K-means5.253400.008130.33502
    PSO39.316300.627810.97912
    SSA39.007100.643160.90803
    ISSA39.108800.648450.90652
    MSSA40.300300.682290.92895
    下载: 导出CSV

    表  8  实验4的分割结果评估

    Table  8.   Evaluation of segmentation results for experiment 4

    算法PSNRSSIMFSIM
    K-means6.097300.101490.52270
    PSO37.963200.598250.93910
    SSA39.386300.689160.96929
    ISSA38.571200.627140.96676
    MSSA39.550900.699450.97200
    下载: 导出CSV

    表  9  实验5的分割结果评估

    Table  9.   Evaluation of segmentation results for experiment 5

    算法PSNRSSIMFSIM
    K-means8.211900.041970.41687
    PSO38.800000.548370.96189
    SSA38.850600.583580.89783
    ISSA37.288000.450270.90825
    MSSA39.180700.600850.89722
    下载: 导出CSV

    表  10  实验6的分割结果评估

    Table  10.   Evaluation of segmentation results for experiment 6

    算法PSNRSSIMFSIM
    K-means6.707900.027500.50615
    PSO39.117200.661990.94426
    SSA39.829900.627550.93491
    ISSA37.795000.548570.93213
    MSSA40.383200.671800.90882
    下载: 导出CSV

    表  11  实验7的分割结果评估

    Table  11.   Evaluation of segmentation results for experiment 7

    算法PSNRSSIMFSIM
    K-means9.086300.050000.50498
    PSO39.300200.596690.93519
    SSA37.144400.439530.91722
    ISSA40.737100.680290.91686
    MSSA40.737100.680290.91686
    下载: 导出CSV

    表  12  实验8的分割结果评估

    Table  12.   Evaluation of segmentation results for experiment 8

    算法PSNRSSIMFSIM
    K-means6.522400.014550.34518
    PSO36.683600.490720.96519
    SSA36.683600.490720.96519
    ISSA37.742200.551240.97844
    MSSA39.945100.646520.98883
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-24
  • 录用日期:  2021-08-27
  • 网络出版日期:  2021-09-13
  • 整期出版日期:  2023-03-30

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