Performance evaluation of multivariate texture descriptor for classification of timber defect

Abstract

This paper presents performance evaluation of texture features based on orientation independent Grey Level Dependence Matrix (GLDM) for the classification of timber defects and clear wood. A series of processes including feature extraction and feature analysis were implemented to facilitate data understanding in order to construct a good feature set that could significantly discriminate between defects and clear wood classes. To further evaluate the discrimination capability of the features extracted, classification experiments were performed on defects and clear wood images of Meranti timber species using common classifiers. The classification performance were further compared between other timber species which are Merbau, KSK and Rubberwood. Results from the analysis reveals that the proposed texture features provide better performance than other feature sets from related works, performs acceptably well across various defect types and across multiple timber species.