A novel texture extraction and classification method for mineral froth images based on complex networks

Abstract

The flotation froth surface texture can be used as an indication to illustrate the production states. A novel froth image texture extraction and classification method based on complex network is presented to obtain the accurate texture features descriptors and facilitate the mineral flotation process monitoring. Firstly, froth images are pre-classified by defining a similarity coefficient. Then, designing the optimum value for the parameter p of Minkowski distance is discussed according to the pre-classification result. A network model of froth image is built utilizing complex network theory. The energy and entropy of the complex network model as texture descriptors is given in terms of the Minkowski distance. Finally, copper froth images captured are used in experiments, and texture feature are extracted. Experiment results show that the presented method can automatically select the optimum values of froth image texture extraction according to their characteristics. It can accurately describe the texture difference of different mineral flotation states, and accurately identify the floatation states.