Fast Solar Image Retrieval and Classification by Fuzzy Rules

The paper proposes a method for classifying and fast retrieving full-disk images of the Sun chromosphere and corona collected by the Solar Dynamics Observatory spacecraft. The method uses a convolutional autoencoder to encode the solar images in the form of a concise semantic hash. The speed of the retrieval comes from the boosting meta-learning to construct a set of fuzzy rules describing the hash parameters. The rules constitute a set of weak classifiers voting for the final image class. This allows for fast retrieving similar images in vast collections of solar images.