利用局部二值模式和方向梯度直方图融合特征对木材缺陷的支持向量机学习分类  

Wood Defect Detection and Classification by Fusion Feature and Support Vector Machine

作　　者：罗微[1] 孙丽萍[1] Luo Wei;Sun Liping(Northeast Forestry University,Harbin 150040,P.R.China)

机构地区：[1]东北林业大学

出　　处：《东北林业大学学报》2019年第6期70-73,共4页Journal of Northeast Forestry University

基　　金：国家林业局林业公益性行业科研专项(201304502)

摘　　要：根据木材缺陷类型及视觉特点的不同,将木材缺陷分为6类,加上正常无缺陷木材共分7类;实验将图像样本转化为灰度图后生成680个训练样本数据集和94个测试样本数据集。在分析单一方向梯度直方图(HOG)特征及局部二值模式(LBP)采用不同核函数对木材缺陷分类性能的基础上,提出局部二值模式和方向梯度直方图融合特征对木材缺陷分类。融合特征经主成分分析并降维,利用支持向量机的4种不同核函数分别验证局部二值模式和方向梯度直方图融合特征对木材缺陷分类的性能。结果表明:利用局部二值模式和方向梯度直方图融合特征比单一缺陷特征具有更高效的分类性能;采用线性核函数及高斯核函数对局部二值模式和方向梯度直方图融合特征进行支持向量机分类,分类准确率分别可达98.9%和97.8%,木材缺陷可实现自动检测分类。According to the different types of wood defects and visual characteristics,the defects were divided into seven types,together with normal non-defective wood.The experiment transforms image samples into gray images and generates 680 training sample data sets and 94 test sample data sets.With the analysis of the characteristics of single histogram of oriented gradient (HOG) and local binary pattern (LBP) using different kernels to classify wood defects,the fusion features of local binary pattern and histogram of oriented gradient were proposed to classify wood defects.By principal component analysis and dimensionality reduction,four different kernel functions of support vector machine were used to validate the performance of local binary pattern and histogram of oriented gradient fusion features for wood defect classification.The fusion feature has more efficient classification performance than single defect feature;the fusion feature of local binary pattern and directional gradient histogram is classified by support vector machine using linear kernel function and Gauss kernel function,and the classification accuracy can reach 98.9% and 97.8%,respectively;therefore,wood defect can be automatically detected and classified.

关 键 词：木材 木材缺陷分类 方向梯度直方图 局部二值模式 支持向量机 

分 类 号：S781.5[农业科学—木材科学与技术] TP274[农业科学—林学]