Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix
Classifying wood species accurately is crucial since incorrect labelling of wood species may incur huge loss to timber industries. An automated wood species recognition system is designed based on image analysis of the wood texture which consists of image acquisition, feature extraction, and classif...
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| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier
2016
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/18690/ https://doi.org/10.1016/j.compag.2016.04.004 |
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| Summary: | Classifying wood species accurately is crucial since incorrect labelling of wood species may incur huge loss to timber industries. An automated wood species recognition system is designed based on image analysis of the wood texture which consists of image acquisition, feature extraction, and classification. There are 100 images captured from each wood sample which are divided into training samples and testing samples. An effective feature extractor is important to extract most discriminant features from the wood texture in order to distinguish the wood species accurately. Therefore, in this paper, a novel feature extractor based on Improved-Basic Gray Level Aura Matrix (I-BGLAM) technique is proposed to extract 136 features from each wood image. Fundamentally, the proposed I-BGLAM feature extractor which focuses on the gray level of the wood images is rotational invariant and has smaller feature dimension since only discriminative features are considered. Then, the proposed system automatically classifies 52 wood species by using backpropagation neural network classifier. The proposed I-BGLAM feature extractor had shown to overcome the limitations of Gray Level Co-occurrence Matrix (GLCM) and conventional BGLAM feature extractors in wood species recognition system. Experiments were performed to determine which dataset would be the most ideal when dividing the 100 wood images into training samples and testing samples. Results showed that the most ideal dataset that should be used is dataset that consists of 80 training samples and 20 test samples. The proposed method showed marked improvement of 97.01% accuracy to the work done previously. |
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