Wood Species Recognition System based on Improved Basic Grey Level Aura Matrix as feature extractor

An automated wood species recognition system is designed to perform wood inspection at custom checkpoints in order to avoid illegal logging. The system that includes image acquisition, feature extraction and classification is able to classify the 52 wood species. There are 100 images taken from the each wood species is then divided into training and testing samples for classification. In order to differentiate the wood species precisely, an effective feature extractor is necessary to extract the most distinguished features from the wood surface. In this research, an Improved Basic Grey Level Aura Matrix (I-BGLAM) technique is proposed to extract 136 features from the wood image. The technique has smaller feature dimension and is rotational invariant due to the considered significant feature extract from the wood image. Support vector machine (SVM) is used to classify the wood species. The proposed system shows good classification accuracy compared to previous works.