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基于多特征融合的花卉种类识别研究

吴笑鑫 高良 闫民 赵方

吴笑鑫, 高良, 闫民, 赵方. 基于多特征融合的花卉种类识别研究[J]. 北京林业大学学报, 2017, 39(4): 86-93. doi: 10.13332/j.1000-1522.20160367
引用本文: 吴笑鑫, 高良, 闫民, 赵方. 基于多特征融合的花卉种类识别研究[J]. 北京林业大学学报, 2017, 39(4): 86-93. doi: 10.13332/j.1000-1522.20160367
WU Xiao-xin, GAO Liang, YAN Min, ZHAO Fang. Flower species recognition based on fusion of multiple features[J]. Journal of Beijing Forestry University, 2017, 39(4): 86-93. doi: 10.13332/j.1000-1522.20160367
Citation: WU Xiao-xin, GAO Liang, YAN Min, ZHAO Fang. Flower species recognition based on fusion of multiple features[J]. Journal of Beijing Forestry University, 2017, 39(4): 86-93. doi: 10.13332/j.1000-1522.20160367

基于多特征融合的花卉种类识别研究

doi: 10.13332/j.1000-1522.20160367
基金项目: 

国家自然科学基金项目 11272061

详细信息
    作者简介:

    吴笑鑫。主要研究方向:图像处理、模式识别。Email:704124637@qq.com  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

    赵方,副教授。主要研究方向:软件工程、模式识别。Email:929554007@qq.com  地址:同上

  • 中图分类号: S688.9;TP391.4

Flower species recognition based on fusion of multiple features

  • 摘要: 花卉种类识别作为植物自动分类识别的重要分支,有着很高的研究和应用价值。针对当前花卉特征描述存在的局限和花卉识别准确率较低的实际情况,以花卉图像为研究对象,首先对复杂背景图像采用基于显著性检测的Grab Cut分割算法进行预处理,得到单一背景图像;然后在提取花卉图像花冠(所有花瓣)颜色和形状特征的基础上,创新性地提取花蕊区域的颜色和形状所包含的特征信息,并将提取到的18个特征融合成单一特征向量。以支持向量机(SVM)算法为基础构建分类器,通过实验确定核函数与最佳参数;对360幅自建花卉样本库(24个种类,每个种类15幅)进行训练和测试,其中240幅作为训练样本,120幅作为测试样本,并与基于不同特征组合的识别方法进行比较。结果表明:本文提出的基于多特征融合的识别方法具有较高的识别准确率,识别率可以达到92.50%。对通用花卉样本库Oxford 17 flower进行训练与测试,选取其中340幅作为训练样本,170幅作为测试样本,取得了较好的识别效果,验证了本文方法的有效性。

     

  • 图  1  图像分割过程

    Figure  1.  Process of image segmentation

    图  2  12×10的HS空间划分

    Figure  2.  Separating HS color space into 12×10 color cells

    图  3  基于多特征融合的花卉种类识别模型

    Figure  3.  Flower image recognition model based on fusion of multiple features

    图  4  24类花卉样本

    Figure  4.  24 species of flower samples used in experiments

    图  5  种类7、9、20对比图

    Figure  5.  Comparison in species 7, species 9 and species 20

    图  6  不同拍摄角度下的玛格丽特花

    Figure  6.  Marguerite flowers from different camera angles

    表  1  不同核函数下的识别率

    Table  1.   Recognition rate of different kernel functions

    训练样本
    Training sample
    测试样本
    Test sample
    核函数
    Kernel function
    识别率
    Recognition rate/%
    240120线性核函数Linear kernel function84.17
    240120多项式核函数Polynomial kernel function82.50
    240120Sigmoid核函数Sigmoid kernel function83.33
    240120径向基核函数Radial basis kernel function85.83
    下载: 导出CSV

    表  2  不同特征组合基于不同分类器的识别率

    Table  2.   Recognition rate of three different classifiers under different combination of features

    分类算法Classifying methodCFSFPCFPSFCF+SFPCF+PSFCF+SF+PCFCF+SF+PSFCF+SF+PCF+PSF
    KNN44.1778.3337.5034.1781.6751.6782.5084.1785.00
    BP神经网络BP neutral network52.5080.8343.3336.6783.3357.5085.8386.6787.50
    SVM58.3385.0046.6740.8386.6763.3388.3389.1792.50
    注:CF代表花冠颜色特征,SF代表花冠形状特征,PCF代表花蕊区域颜色特征,PSF代表花蕊区域形状特征。Notes: CF represents corolla color feature, SF represents corolla shape feature, PCF represents color feature in pistil area, PSF represents shape feature in pistil area.
    下载: 导出CSV

    表  3  不同方法对每类花卉的识别率

    Table  3.   Recognition rate for each class of flower based on different methods

    类别
    Flower species
    识别率1
    Recognition rate 1/%
    识别率2
    Recognition rate 2/%
    1100100
    2100100
    38080
    48080
    580100
    6100100
    76080
    8100100
    96080
    10100100
    118080
    1280100
    13100100
    14100100
    158080
    1680100
    17100100
    18100100
    198080
    204080
    21100100
    22100100
    23100100
    248080
    注:识别率1,基于花冠颜色特征与花冠形状特征的识别率;识别率2,基于花冠颜色特征、花冠形状特征和花蕊区域特征的识别率。Notes: recognition rate 1, recognition rate based on color and shape features of corolla region; recognition rate 2, recognition rate based on color and shape features of corolla region and pistil area.
    下载: 导出CSV

    表  4  基于不同方法的花卉识别率

    Table  4.   Recognition rate based on different methods

    %
    采用方法Method识别率Recognition rate
    本文方法Method proposed80.0
    文献[5] Literature [5]72.8
    文献[6] Literature [6]76.3
    文献[7] Literature [7]79.4
    文献[8] Literature [8]80.7
    下载: 导出CSV
  • [1] 柯逍, 陈小芬, 李绍滋.基于多特征融合的花卉图像检索[J].计算机科学, 2010, 37(11):282-286. doi: 10.3969/j.issn.1002-137X.2010.11.068

    KE X, CHEN X F, LI S Z. Flower image retrieval based on multi-features fusion[J]. Computer Science, 2010, 37(11): 282-286. doi: 10.3969/j.issn.1002-137X.2010.11.068
    [2] 周伟, 武港山.基于显著图的花卉图像分类算法研究[J].计算机技术与发展, 2011, 21(11):15-18. doi: 10.3969/j.issn.1673-629X.2011.11.005

    ZHOU W, WU G S. Research on saliency map based flower image classification algorithm[J]. Computer Technology and Development, 2011, 21(11):15-18. doi: 10.3969/j.issn.1673-629X.2011.11.005
    [3] 张娟, 黄心渊.基于图像分析的梅花品种识别研究[J].北京林业大学学报, 2012, 34(1):96-104. http://j.bjfu.edu.cn/article/id/9711

    ZHANG J, HUANG X Y. Species identification of Prunus mume based on image analysis[J]. Journal of Beijing Forestry University, 2012, 34(1):96-104. http://j.bjfu.edu.cn/article/id/9711
    [4] 白帆, 郑慧峰, 沈平平, 等.基于花朵特征编码归类的植物种类识别方法[J].浙江大学学报(工学版), 2015, 49(10):1902-1908. http://d.old.wanfangdata.com.cn/Periodical/zjdxxb-gx201510011

    BAI F, ZHENG H F, SHEN P P, et al. Plant species identification method based on flower feature coding classification[J]. Journal of Zhejiang University(Engineering Science), 2015, 49(10):1902-1908. http://d.old.wanfangdata.com.cn/Periodical/zjdxxb-gx201510011
    [5] NILSBACK M E, ZISSERMAN A. Automated flower classification over a large number of classes[C]//Proceedings of Indian Conference on Computer Vision, Graphics & Image Processing. Bhubaneswar: IEEE, 2008: 722-729.
    [6] NILSBACK M E, ZISSERMAN A. Delving deeper into the whorl of flower segmentation[J]. Image & Vision Computing, 2010, 28(6):1049-1062. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=8b2bd882ca3a14c96af35700d88163ca
    [7] CHAI Y, LEMPITSKY V, ZISSERMAN A. BiCoS: a Bi-level co-segmentation method for image classification[C]//Proceedings of IEEE International Conference on Computer Vision. Barcelona: IEEE, 2011: 2579-2586.
    [8] ANGELOVA A, ZHU S. Efficient object detection and segmentation for fine-grained recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington: IEEE Computer Society, 2013: 811-818.
    [9] YANIKOGLU B, YANIKOGLU B, APTOULA E, et al. Sabanci-Okan system at ImageClef 2012: combining features and classifiers for plant identification[C]. Rome: CLEF, 2012.
    [10] 王丽君, 淮永建, 彭月橙.基于叶片图像多特征融合的观叶植物种类识别[J].北京林业大学学报, 2015, 37(1):55-61. doi: 10.13332/j.cnki.jbfu.2015.01.006

    WANG L J, HUAI Y J, PENG Y C. Method of identification of foliage from plants based on extraction of multiple features of leaf images[J]. Journal of Beijing Forestry University, 2015, 37(1):55-61. doi: 10.13332/j.cnki.jbfu.2015.01.006
    [11] ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods.[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(11):2274-2282. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=218e2dd693ea07f7be3f296ed4e6aaba
    [12] YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington: IEEE Computer Society, 2013: 3166-3173.
    [13] ROTHER C, KOLMOGOROV V, BLAKE A. GrabCut: interactive foreground extraction using iterated graph cuts[J]. Acm Transactions on Graphics, 2004, 23(3):309-314. doi: 10.1145/1015706.1015720
    [14] 裴勇.基于数字图像的花卉种类识别技术研究[D].北京: 北京林业大学, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10022-1011134451.htm

    PEI Y. Study on the flower identification technology with digital images[D].Beijing: Beijing Forestry University, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10022-1011134451.htm
    [15] GONZALEZ R C, WINTZ P. Digital image processing[J]. Prentice Hall International, 2002, 28(4):484-486. http://d.old.wanfangdata.com.cn/Periodical/bjykdxxx201601025
    [16] 张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000, 26(1):32-42. doi: 10.3969/j.issn.1003-8930.2000.01.008

    ZHANG X G. Introduction to statistical learning theory and support vector machines[J]. Acta Automatica Sinica, 2000, 26(1):32-42. doi: 10.3969/j.issn.1003-8930.2000.01.008
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出版历程
  • 收稿日期:  2016-11-08
  • 修回日期:  2016-12-26
  • 刊出日期:  2017-04-01

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