Using Two Stage Classification for Improved Tropical Wood Species Recognition System

  • Anis Salwa Mohd Khairuddin
  • Marzuki Khalid
  • Rubiyah Yusof
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 11)

Abstract

An automated wood recognition system is designed based on five stages: data acquisition, pre-processing images, feature extraction, pre classification and classification. The proposed system is able to identify 52 types of wood species based on wood features extracted using Basic Grey Level Aura Matrix (BGLAM) technique and statistical properties of pores distribution (SPPD) technique. The features obtained from both feature extractors are fused together and will determine the classification between the various wood species. In order to enhance the class separability, a pre-classification stage is developed which includes clustering and dimension reduction. K-means clustering is introduced to cluster the 52 wood species. As for dimension reduction, we proposed linear discriminant analysis (LDA) to solve linear data and kernel discriminant analysis/ generalized singular value decomposition (KDA/GSVD) to solve nonlinearly structured data. For final classification, K-Nearest Neighbour (KNN) classifier is implemented to classify the wood species.

Keywords

wood recognition LDA KDA/GSVD K-means cluster kNN classifier 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anis Salwa Mohd Khairuddin
    • 2
  • Marzuki Khalid
    • 1
  • Rubiyah Yusof
    • 1
  1. 1.Center for Artificial Intelligence and RoboticsUniversiti Teknologi MalaysiaKuala LumpurMalaysia
  2. 2.Department of Electrical EngineeringUniversity of MalayaKuala LumpurMalaysia

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