Wood Species Recognition Based on SIFT Keypoint Histogram

TL;DR

This paper introduces a novel wood species recognition method using SIFT keypoint histograms, which improves accuracy and reduces reliance on expert knowledge by leveraging image processing and machine learning techniques.

Contribution

The paper proposes a new approach combining SIFT, clustering, and classification models for wood recognition, outperforming existing methods like GLCM and LBP.

Findings

Higher recognition accuracy than traditional methods

Effective use of SIFT keypoints and clustering for feature extraction

Validation with multiple classifiers showing consistent performance

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.