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林业科学 ›› 2013, Vol. 49 ›› Issue (6): 122-128.doi: 10.11707/j.1001-7488.20130617

• 论文与研究报告 • 上一篇    下一篇

基于PCA+FisherTrees特征融合的木材识别

刘子豪1, 汪杭军2   

  1. 1. 浙江农林大学信息工程学院 临安 311300;
    2. 浙江农林大学天目学院 临安 311300
  • 收稿日期:2012-08-06 修回日期:2012-09-24 出版日期:2013-06-25 发布日期:2013-07-16
  • 通讯作者: 汪杭军
  • 基金资助:

    国家自然科学基金项目(30972361); 浙江省教育厅重大科研攻关项目(ZD2009002); 浙江省自然科学基金项目(Y13C160027); 浙江农林大学研究生科研创新项目(3122013240224)。

Wood Identification Based on Feature Fusion of PCA and FisherTrees

Liu Zihao1, Wang Hangjun2   

  1. 1. School of Information Engineering, Zhejiang A & F University Lin'an 311300;
    2. Tianmu College of Zhejiang A & F University Lin'an 311300
  • Received:2012-08-06 Revised:2012-09-24 Online:2013-06-25 Published:2013-07-16

摘要:

提出一种高效的基于PCA和FisherTrees特征融合的木材识别方法,首先把训练样本分别投影到PCA和FisherTrees空间,得到PCA特征和FisherTrees特征; 然后通过算术均值、交换转置均值和加权均值进行特征融合,将融合后的特征应用不同距离函数的分类器进行分类。结果表明: 通过交换转置均值融合PCA和FisherTrees特征,然后使用余弦角分类器能获得最好的识别效果。

关键词: 特征融合, 主成分分析(PCA), 费舍尔树(FisherTrees), 木材识别

Abstract:

A new efficient method based on feature fusion of PCA and FisherTrees for wood identification was proposed in this paper. Firstly, the training samples were projected into PCA and FisherTrees space respectively to form the PCA and FisherTrees features, then the two features were fused through three ways, i.e. arithmetic mean, swapping transposition mean and weighting mean. Finally, the feature fusion was applied to classify with different distance functions. The experimental results showed that the new method had a higher recognition rate and was more efficient compared with the tradition subspace methods. The best identification result could be obtained by features fusion of PCA and FisherTrees with swapping transposition mean and by the cosine distance function classifier.

Key words: feature fusion, principle component analysis(PCA), FisherTrees, wood identification

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