Wood species recognition based on SIFT keypoint histogram

Abstract

Traditionally, only experts who are equipped with professional knowledge and rich experience are able to recognize different species of wood. Applying image processing techniques for wood species recognition can not only reduce the expense to train qualified identifiers, but also increase the recognition accuracy. In this paper, a wood species recognition technique base on Scale Invariant Feature Transformation (SIFT) keypoint histogram is proposed. We use first the SIFT algorithm to extract keypoints from wood cross section images, and then k-means and k-means++ algorithms are used for clustering. Using the clustering results, an SIFT keypoints histogram is calculated for each wood image. Furthermore, several classification models, including Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are used to verify the performance of the method. Finally, through comparing with other prevalent wood recognition methods such as GLCM and LBP, results show that our scheme achieves higher accuracy.