Forest species recognition using macroscopic images
The recognition of forest species is a very challenging task that generally requires well-trained human specialists. However, few reach good accuracy in classification due to the time taken for their training; then they are not enough to meet the industry demands. Computer vision systems are a very interesting alternative for this case. The construction of a reliable classification system is not a trivial task, though. In the case of forest species, one must deal with the great intra-class variability and also the lack of a public available database for training and testing the classifiers. To cope with such a variability, in this work, we propose a two-level divide-and-conquer classification strategy where the image is first divided into several sub-images which are classified independently. In the lower level, all the decisions of the different classifiers, trained with different features, are combined through a fusion rule to generate a decision for the sub-image. The higher-level fusion combines all these partial decisions for the sub-images to produce a final decision. Besides the classification system we also extended our previous database, which now is composed of 41 species of Brazilian flora. It is available upon request for research purposes. A series of experiments show that the proposed strategy achieves compelling results. Compared to the best single classifier, which is a SVM trained with a texture-based feature set, the divide-and-conquer strategy improves the recognition rate in about 9 percentage points, while the mean improvement observed with SVMs trained on different descriptors was about 19 percentage points. The best recognition rate achieved in this work was 97.77%.
textural descriptors fusion of classifiers two-level classification strategy forest species classification