Evaluation of a model using local features and a codebook for wood identification

Abstract

We designed a model for wood identification based on scale-invariant feature transform (SIFT) descriptors and a codebook. A dataset consisting of cross-sectional optical micrographs of the Lauraceae family including 39 species was used for identification. The bag- of-features (BOF) model was superior to the model combined SIFT descriptors with a classifier. Among the four classifiers applied to both models, the support vector machine (SVM) achieved the best identification performance with 99.4% accuracy. From the feature importance calculated by the random forests and the inverse document frequency (IDF) score, it was also confirmed that cell corner-based features are more informative for the identification of Lauraceae. In particular, cell corners in vessels are not only important for species identification, but also reveal that they are species-specific features. The computer vision-based model was suitable for Lauraceae identification and enabled the quantification of anatomical structures that are not possible with conventional visual inspection for wood identification.