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Optimization in Automatic Classification of Wood Species using Gray Level Co-Occurence Matrix and K-Nearest Neighbor

By Ahmad Fariz Hasan, Mohd Fairus Ahmad, Mohd Nasir Ayob, Shamsul Amir, Abdul Rais, Nor Hidayah Saad, Amar Faiz, Zainal Abidin, Ali Abussal, Nur Anis Nordin, Syahrul Hisham Mohamad, Safirin Mohd Karis and Hazriq Izzuan Jaafar


Abstract — This paper proposed an application of Binary Particle Swarm Optimization in automatic classification of wood species. The images of wood species are taken from Universiti Teknologi Malaysia’s CAIRO Wood Database which consists of 25 species. The features of the images are extracted using Gray Level Co-Occurrence Matrix. Then, Binary Particle Swarm Optimization is use to optimize feature selection and parameters related to it. The result indicates that the proposed approach obtained a better result compared to previous literatures with fewer features used as input for the classifier. Index Terms — binary particle swarm optimization; computational intelligence; gray level co-occurrence matrix; k-nereast neighbour; optimization; pattern recognition; wood recogniiton 1

Year: 2014
OAI identifier: oai:CiteSeerX.psu:
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