Identifying Minerals from Image Using Out-of-Distribution Artificial Intelligence-Based Model

Abstract

Deep learning has increasingly been used to identify minerals. However, deep learning can only be used to identify minerals within the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, this study introduces the approach that combines a One-Class Support Vector Machine (OCSVM) with the ResNet architecture for out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the first two layers obtained from the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set’s distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event that OCSVM identifies a mineral outside of the training set’s distribution, it is categorized as an unclassified or ‘unknown’ mineral. Empirical results substantiate the method’s capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.