Selection of features for the classification of wood board defects

We compare three methods for selecting features that have recently been applied to the classification of defects on wood boards. A first method is based on statistical measures to determine how well features differentiate between classes. A second method consists of leaving out each of the features in turn and performing classification on the remaining features. A third method is based on genetic algorithms. The performances of the three methods are measured on a database containing color images of 900 pine wood defects classified into 9 categories. The best overall performance obtained was 93% of correct classifications on a test set, with only 20 out of 72 original features