An unsupervised method for extracting semantic features of flotation froth images

The froth feature extraction plays an important role in flotation circuit monitoring. Given the low efficiency of hand-crafted features and the poor interpretability of high-dimensional features extracted by convolutional neural networks, an unsupervised method for extracting human-understandable semantic features of flotation froth images is proposed in this paper. First, based on the combination of generative adversarial networks and autoencoder, we design a new network structure that maps the froth images data space to the latent semantic space. Then through network training with the historical froth image data, a matrix with rich latent semantics is constructed. Finally, the semantic features of froth images can be automatically extracted by decomposing the constructed matrix. As demonstrated in the industrial experiment, the extracted semantic features can not only be visually interpreted but also can be effectively used in flotation condition recognition and grade prediction. This is the first report that generative adversarial networks can be used to extract froth image features and improve the semantic interpretability of features.

Automated summary

This paper proposes a method to convert flotation froth image data into a latent semantic space and automatically extract semantic features. Experimental results show that the extracted features can be visually interpreted and effectively used in flotation condition recognition and grade prediction. This is the first report demonstrating the use of generative adversarial networks to improve the interpretability of froth image features.