Evaluating the classification of images from geoscience papers using small data

Image classification becomes a very challenging task when it involves classes that have shared characteristics and few data are available for training the classifier. Considering this problem, in this work we adopt a case study based on images from geoscience papers and investigate how different features can be combined in order to improve image classification results. In our investigation, we present a tool for evaluating class separability based on the position of the samples in a two-dimensional map according to different features. Moreover, we investigate the usefulness of classifiers’ membership probabilities for our scenario, validating if they can be used as reliable measures of the confidence in the predicted labels. Our experimental results show that it is possible to take advantage of deep learning models’ ability to learn discriminating features from data and combine them with hand-crafted features to improve classification. With this feature combination, we trained a Support Vector Machine (SVM) classifier whose results are better than the ones achieved using only deep learning.