A k-Shot Learning Approach to Recognition of Flotation Froth Conditions with Convolutional Neural Networks

Abstract

In this paper it is shown that operational regimes in flotation plants can be identified by machine learning models, based on the availability of just a few tens of froth images of each regime. This is accomplished through the generation of synthetic images that can be used to train machine learning models used in froth pattern identification. The study was based on a small set of images from a platinum group metals plant. Synthetic images were generated with two convolutional neural networks. Features extracted from synthetic and real images with local binary patterns and AlexNet were indistinguishable from each other. In addition, features from a small set of 30 synthetic images that were used as predictors in a random forest model performed similarly to the same features extracted from real images, even from considerably larger data set. This suggests that the use of synthetic froth images generated by deep learning models can serve as the basis for few shot learning models.