A Comparative Study for Texture Classification Techniques on Wood Species Recognition Problem

Wood species recognition is a texture classification problem that has yet to be well studied. The textures observed on the cross section surface of the wood samples can be used to identify the species of the wood. In this paper, we tested various texture classification techniques, i.e. grey level co-occurrence matrices (GLCM), Gabor filters, combined GLCM and Gabor filters as well as covariance matrix. The experiments are conducted on 512 × 512 images of the six wood species from the CAIRO wood dataset. The experimental results show that the covariance matrix produced using the feature images generated by the Gabor filters is 85% compared to 78.33% for the raw GLCM, 73.33% for the Gabor filters and 76.67% for the combined GLCM and Gabor filters. The experimental results show that the covariance matrix has the best recognition rate.