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一种融合聚类和分类算法的树木图像多目标优化分割方法

王晓松 杨刚

王晓松, 杨刚. 一种融合聚类和分类算法的树木图像多目标优化分割方法[J]. 北京林业大学学报, 2018, 40(12): 124-131. doi: 10.13332/j.1000-1522.20180160
引用本文: 王晓松, 杨刚. 一种融合聚类和分类算法的树木图像多目标优化分割方法[J]. 北京林业大学学报, 2018, 40(12): 124-131. doi: 10.13332/j.1000-1522.20180160
Wang Xiaosong, Yang Gang. A multi-objective optimization segmentation method for tree image based on fusion clustering and classification algorithm[J]. Journal of Beijing Forestry University, 2018, 40(12): 124-131. doi: 10.13332/j.1000-1522.20180160
Citation: Wang Xiaosong, Yang Gang. A multi-objective optimization segmentation method for tree image based on fusion clustering and classification algorithm[J]. Journal of Beijing Forestry University, 2018, 40(12): 124-131. doi: 10.13332/j.1000-1522.20180160

一种融合聚类和分类算法的树木图像多目标优化分割方法

doi: 10.13332/j.1000-1522.20180160
基金项目: 

国家自然科学基金项目 61602277

国家自然科学基金项目 61773244

山东省自然科学基金项目 ZR2016GB06

国家自然科学基金项目 61772319

详细信息
    作者简介:

    王晓松,博士,副教授。主要研究方向:图像处理、数字林业。Email:morningpine@163.com  地址:264005山东省烟台市滨海中路191号山东工商学院管理科学与工程学院

    责任作者:

    杨刚,副教授。主要研究方向:计算机图形学、数字林业。Email:yanggang@bjfu.edu.cn  地址:100083北京市海淀区清华东路35号北京林业大学信息学院

  • 中图分类号: S757.2;TP391

A multi-objective optimization segmentation method for tree image based on fusion clustering and classification algorithm

  • 摘要: 目的结合树木图像颜色和纹理特征,融合聚类和分类算法对树木图像进行多目标优化分割,从而提高自然背景下树木图像分割的准确性。方法首先,利用MSCC框架理论,解决聚类和分类目标函数同时依赖于聚类中心的问题。然后,分别选定聚类性能评价指标函数和分类性能评价指标函数。最后,采用多目标进化优化方法——NSGA-II算法进行优化,得到Pareto前端最优解集,并通过计算聚类有效性指数I的最大值,寻找最优解决方案。选择具有代表性的法国梧桐、侧柏、松树和杏树等自然背景下拍摄的4幅图像作为样本。分别采用K-means、Fuzzy C-means、对聚类目标函数进行单目标优化,采用MOPSO方法进行多目标优化,以及NSGA-II方法进行多目标优化等5种方法对样本图像进行分割比较。结果在聚类中心数量相同、种群大小相同、遗传代数相同的条件下,指数I的值表明本文提出的分割方法优势显著。对于4类不同样本图像分割的指数I值进行对比可知,以HF指数为单目标函数进行遗传优化的结果优于单一使用K-means和FCM算法;MOPSO多目标优化方法分割结果优于单目标优化结果;基于NSGA-II优化的多目标函数分割结果又优于MOPSO多目标优化结果。结论融合聚类和分类算法构建聚类性能评价指标函数和分类评价性能指标函数,并采用非支配排序遗传算法对多目标函数进行优化,能更好地保留树木图像的颜色和纹理特征,分割准确率显著提高。

     

  • 图  1  两个目标函数的帕累托前端最优解集

    Figure  1.  Pareto front of a set of solutions in a two objective space

    图  2  NSGA-II优化基本流程图

    Figure  2.  Flowchart of NSGA-II optimization

    图  3  染色体编码

    Figure  3.  Chromosome coding

    图  4  不同分割方法分割结果图

    Figure  4.  Comparison of segmentation results by different segmentation methods

    表  1  NSGA-II实验参数设置

    Table  1.   Parameter settings for the experiment

    参数名称Parameter 参数值Setting
    种群大小Population size 20
    遗传代数Number of generations 40
    选择算子Selection operator 二进制锦标赛Binary tournament
    交叉算子Crossover operator SBX算子SBX operator
    变异算子Mutation operator 多项式变异算子Polynomial mutation operator
    交叉概率Probability of crossover 0.8
    变异概率Probability of mutation 1/染色体长度1/length of chromosome
    交叉索引值Crossover index value 2.0
    变异索引值Mutation index value 5.0
    下载: 导出CSV

    表  2  不同分割方法指数I值比较

    Table  2.   Comparison of I index value by different segmentation methods

    样本Sample K-means (K=3) FCM (m=2, K=3) HF指数单目标GA优化HF index single objective optimization MSCC多目标粒子群优化Two objective MOPSO optimization MSCC多目标NSGA-II优化Two objective NSGA-II optimization
    法国梧桐Platanus orientalis 67.56 74.87 85.32 91.36 96.28
    侧柏Platycladus orientalis 50.56 67.45 74.65 85.43 90.32
    松树Pinus sp. 40.56 41.45 50.32 72.39 83.87
    杏树Armeniaca sp. 59.67 65.34 69.56 78.82 87.98
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-15
  • 修回日期:  2018-09-17
  • 刊出日期:  2018-12-01

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