@article{(Open Science Index):https://publications.waset.org/pdf/14323, title = {Improved Tropical Wood Species Recognition System based on Multi-feature Extractor and Classifier}, author = {Marzuki Khalid and RubiyahYusof and AnisSalwaMohdKhairuddin}, country = {}, institution = {}, abstract = {An automated wood recognition system is designed to classify tropical wood species.The wood features are extracted based on two feature extractors: Basic Grey Level Aura Matrix (BGLAM) technique and statistical properties of pores distribution (SPPD) technique. Due to the nonlinearity of the tropical wood species separation boundaries, a pre classification stage is proposed which consists ofKmeans clusteringand kernel discriminant analysis (KDA). Finally, Linear Discriminant Analysis (LDA) classifier and KNearest Neighbour (KNN) are implemented for comparison purposes. The study involves comparison of the system with and without pre classification using KNN classifier and LDA classifier.The results show that the inclusion of the pre classification stage has improved the accuracy of both the LDA and KNN classifiers by more than 12%.}, journal = {International Journal of Electrical and Computer Engineering}, volume = {5}, number = {11}, year = {2011}, pages = {1495 - 1501}, ee = {https://publications.waset.org/pdf/14323}, url = {https://publications.waset.org/vol/59}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 59, 2011}, }