Abstract: This study introduces a analysis on the comparison of feature extraction techniques on the segmented wood defects. The method used is to classify four types of wood defects, namely knot, crack, holes and algae. The wood defect dataset used in this research consisted of 145 images were obtained from various sources. Fuzzy C-Means (FCM) is employed to segment wood defects into four clusters. Six types of feature extraction techniques namely Colour Histogram, Colour Coherence Vector, Local Binary Pattern, Gacfrdfbor Transform, Discrete Wavelet Frame and Gray Level Co-occurrence Matric are employed to describe the images feature. The performances of Support Vector Machines (SVM), Bayes Networks and tree-based classifiers are comparedon the different defects and the classifiers performances for each extraction technique are investigated. The experiment shows promising results with the highest classification accuracy of 94.5%, achieved by Random Forest classifier using Colour Histogram features. The proposed framework is useful in the automation of the detection of wood defects and is a superior alternative to manual selection and classification in the wood quality control.
Wan Siti Halimatul Munirah Wan Ahmad, Hau-Lee Tong, Hu Ng, Timothy Tzen-Vun Yap and Mohammad Faizal Ahmad Fauzi, 2016. Comparison of Feature Extraction Techniques on Segmented Wood Defects. Research Journal of Applied Sciences, 11: 374-379.