Comparison of evolutionary and conventional feature extraction methods for malt classification
Vic Ciesielski, Brian Lam, Minh Luan Nguyen · 2012
Our primary motivation in this paper is to determine whether evolved texture feature extraction programs are competitive with human derived programs for a difficult real world texture classification problem. The problem involves distinguishing images of three classes of bulk malt. There are subtle differences between the three classes. We have used a number of human derived methods, Haralick, Gabor, Haar, histogram and Galloway, to get feature vectors for the malt problem. We have also used a number of feature extraction programs that were evolved from thirteen Brodatz textures. We performed classification with a 1 nearest neighbour classifier. The evolved features gave an accuracy of 67% which is considerably better than the 53% achieved with the Haar features, but not as good as the 77% achieved with the Galloway features. Analysis of the evolved features suggested that they are capturing some texture regularities not captured by the human derived methods. We conclude that the evolved features are competitive with the human derived features and can provide enhanced accuracy when used in conjunction with human derived features.