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首页> 外文期刊>Journal of Engineering & Applied Sciences >Evaluation of Feature Extraction and Selection Techniques for the Classification of Wood Defect Images
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Evaluation of Feature Extraction and Selection Techniques for the Classification of Wood Defect Images

机译：用于木材缺陷图像分类的特征提取和选择技术的评价

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摘要

The main objective is to evaluate different feature extraction and selection techniques as well as classification performances for the wood defect images. This study presents a classification system to classify the defect images from a database provided by a wood factory. This database consists of 1498 defect images and they are classified using Support Vector Machine (SVM), J48, random forest and K-NN classifiers. The features for each defect image are extracted using six types of feature extraction techniques. Feature selection methods are used to choose the features according to their significance. From the findings, it can be observed that Ranker method produced the best performance for most of the feature extraction techniques and classifiers. This directly indicates that all the extracted features have significant contribution. For SVM, it is tested with three different settings: linear, RBF and polynomial. The highest classification rate is obtained by using Gray Level Co-occurrence Matrix (GLCM) with SVM polynomial. For J48 and random forest classifier, features computed using Colour Coherence Vector (CCV) yielded the best measure, whilst for K-NN, it is Gabor features which performed best. Besides 89.85% of case crack are correctly classified, 38.63% for fungus, 16.48% for knot, 88.06% for worm holes and 51.61% for watermark case. For defect cases other than crack, it is observed that the number of misclassification cases is biased on crack case. The proposed methodology can be applied to create an automated visual inspection system for detection of semi-finished wood defect in the wood industry.
机译：主要目的是评估木材缺陷图像的不同特征提取和选择技术以及分类性能。本研究提出了一个分类系统，用于将缺陷图像与木工厂提供的数据库分类。该数据库由1498个缺陷图像组成，它们使用支持向量机（SVM），J48，随机林和K-NN分类器进行分类。使用六种类型的特征提取技术提取每个缺陷图像的特征。特征选择方法用于根据其意义选择特征。从调查结果中，可以观察到Ranker方法为大多数特征提取技术和分类器产生了最佳性能。这直接表明所有提取的功能都具有显着的贡献。对于SVM，它用三种不同的设置进行了测试：线性，RBF和多项式。通过使用具有SVM多项式的灰度共生矩阵（GLCM）获得最高分类率。对于J48和随机森林分类器，使用颜色相干载体（CCV）计算的功能产生了最佳测量，而K-NN则是最佳的Gabor功能。除了89.85％的案例裂缝是否正确分类，针刺38.63％，结38.63％，蜗杆孔的88.06％，水印案件为51.61％。对于除裂缝以外的缺陷病例，观察到错误分类病例的数量在裂缝案例上偏置。所提出的方法可以应用于创建一种自动视觉检测系统，用于检测木材工业中半成品缺陷。

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