Complex networks-based texture extraction and classification method for mineral flotation froth images

• A new complex networks-based texture extraction approach is presented.
• Complex network modeling method of froth images using the Minkowski distance.
• Texture features of copper froth images at different production states are extracted.
• Detailed experiments are performed to validate the effectiveness.
• The proposed method can classify different production states for froth flotation.

With recent improvements in instrumentation and computer infrastructure, machine vision technologies have produced innovative approaches for controlling and monitoring mineral flotation process. In order to efficiently analyze froth image, froth texture is used to accurately and rapidly extract froth characteristics based on the statistics of image pixels. The characterization and identification of texture require a method that can express the context surrounding each pixel by combining local and global texture characteristics. To extract the distinctive froth texture features in different production states, a novel complex networks-based texture extraction and classification method for froth imaging is proposed in this paper. A network model is constructed by expressing pixels as network nodes and similarities between pixels as network links. This method automatically sets the optimal algorithm parameters for the complex network modeling of the froth images according to bubble sizes by using the Minkowski distance. Energy and entropy measurements are used to quantify the properties of the connectivity and topology of the froth-image network model. Copper froth images at different production states extracted from the flotation monitoring system in a flotation plant are used to test the froth-image network model. The experimental results show that the proposed method accurately describes the froth image texture and also robustly classifies the different production states.