Automatic Classification of Wood Texture Using Local Binary Pattern &amp; Fuzzy K-Nearest Neighbor

Khairuddin, Ismail Mohd; Abuassal, Ali; Abdelrahim, Ali; Abidin, Amar Faiz Zainal; Mohamad, Syahrul Hisham; Alsawi, Mutaz; Nordin, Nur Anis; Jaafar, Hazriq Izzuan

doi:10.4028/www.scientific.net/amr.903.315

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2022

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“…Several studies have used Local Binary Pattern (LBP) to detect timber flaws [10]. LBP can accurately recognize and detect timber defects [11][12][13][14]. The technique was considered superior for feature extraction to other techniques based on the research [15].…”

Section: Introductionmentioning

confidence: 99%

Local Texture Representation for Timber Defect Recognition based on Variation of LBP

Rahiddin¹,

Hashim²,

Salahuddin³

et al. 2022

IJACSA

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This paper evaluates timber defect classification performance across four various Local Binary Patterns (LBP). The light and heavy timber used in the study are Rubberwood, KSK, Merbau, and Meranti, and eight natural timber defects involved; bark pocket, blue stain, borer holes, brown stain, knot, rot, split, and wane. A series of LBP feature sets were created by employing the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP in a phase of feature extraction procedures. Several common classifiers were used to further separate the timber defect classes, which are Artificial Neural Network (ANN), J48 Decision Tree (J48), and K-Nearest Neighbor (KNN). Uniform LBP with ANN classifier provides the best performance at 63.4%, superior to all other LBP types. Features from Merbau provide the greatest F-measure when comparing the performance of the ANN classifier with Uniform LBP across timber fault classes and clean wood, surpassing other feature sets.

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