Sum and Difference Histograms for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of automated visual inspection
Computer Vision and Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Neural Networks - 2005 Special issue: IJCNN 2005
Cascaded and hierarchical neural networks for classifying surface images of marble slabs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Automatic system for quality-based classification of marble textures
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic classification of granite tiles through colour and texture features
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, a new hierarchical classification method based on the use of various types of AdaBoost classification algorithms is proposed for automatic classification of marble slab images according to their quality. At first, features are extracted using the sum and difference histograms method and, at the second stage, different versions of the AdaBoost algorithms are used as classifiers together with those extracted features in a proposed hierarchical fashion. Performance of the proposed method is compared against performances of different types of neural network classifiers and a support vector machine (SVM) classifier. Computational results show that the proposed hierarchical structure employing AdaBoost algorithms performs superior to neural networks and the SVM classifier for classifying marble slab images in our large and diversified data set.