Ensemble of Artificial Neural Networks and K-Nearest Neighbor for classification of granite images

Abstract

This study attempted to develop a granite quality classification model by comparing colour, texture, and ensemble of colour and texture. An average of 120 images was taken for each verities of grade A, grade B, grade C. A greyscale coexistence matrix was used for texture extraction and a colour histogram for colour extraction. Five textures and six colour features were extracted from each granite image for classification. To build the classification KNN, ANN and ensemble of KNN and ANN are examined. Based on the experiment ensemble of ANN and KNN model outperform good result by the combined texture and colour features using sequential forward feature selection (SFFS) methods. An average accuracy of 85.3%, 93.6%, and 95.8% is achieved for KNN, ANN and ensemble of KNN and ANN respectively. Granite fractures and vines of the images have a strong impact on the performance of the classifier.