Global and Local Features for Char Image Classification

Abstract

The use of image analysis in understanding how powdered coal burns during the combustion plays a significant role in setting combustion parameters. During the pulverised coal combustion, char particles are produced by devolatising coal and represent the dominant stage in the combustion process. The pyrolysis produces different char morphologies that determine coal reactivity affecting the performance of coal combustion in power plants and the emissions of carbon dioxide, CO2. In this paper, an automatic char classification model is proposed using supervised learning. A general classification model is trained given a set of char particles classified by an expert. In particular, Support Vector Machine (SVM) and Random Forest are the trained classifiers. Two types of features are evaluated to built classification models: local and global. Local features are calculated using the Scale-Invariant Transform Feature (SIFT). Global features are defined based on the morphology classification by the International Committee for Coal and Organic Petrology (ICCP). Each classifier is trained by SVM or Random Forest and evaluated using a 10-fold cross-validation. The 70% of data is used as training set and the rest as testing set. A total of 2928 char-particle images are used for evaluating performance of classification models. Additionally, evaluation of model generalisation capability is done using a test set of 732 char particle images. Results showed that global features – defined by the application domain – increase significantly the accuracy of classifiers. Also, global features have more generalisation power than local features. Local features lack of meaning in the application domain and classifiers build with local features – such as SIFT – depend crucially on the training set.