基于PCA+FisherTrees特征融合的木材识别

doi:10.11707/j.1001-7488.20130617

Abstract

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

CLC Number:

S781
TP391

Liu Zihao, Wang Hangjun. Wood Identification Based on Feature Fusion of PCA and FisherTrees[J]. Scientia Silvae Sinicae, 2013, 49(6): 122-128.

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Wood Identification Based on Feature Fusion of PCA and FisherTrees

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