Automatic forest species recognition based on multiple feature sets

In this paper we investigate the use of multiple feature sets for automatic forest species recognition. In order to accomplish this, different feature sets are extracted, evaluated, and combined into a framework based on two approaches: image segmentation and multiple feature sets. The experimental results on microscopic and macroscopic images of wood indicate that the recognition rates can be improved from 74.58% to about 95.68% and from 68.69% to 88.90%, respectively. In addition, they reveal us the importance of exploring different window sizes and appropriate local estimation functions for the LPQ descriptor, further than the classical uniform and gaussian functions.