An astronomical image content-based recommendation system using combined deep learning models in a fully unsupervised mode

TL;DR

This paper presents an unsupervised deep learning system for astronomical image clustering and recommendation, capable of mapping and grouping large images without labeled data, aiding in faster image labeling and content filtering.

Contribution

The authors develop a novel fully unsupervised pipeline combining deep autoencoders and self-organizing maps for astronomical image clustering and recommendation, outperforming supervised methods in some aspects.

Findings

System effectively maps images onto a 2D grid.

Unsupervised method achieves comparable performance to supervised approaches.

Enables faster image labeling and content-based filtering.

Abstract

We have developed a method that maps large astronomical images onto a two-dimensional map and clusters them. A combination of various state-of-the-art machine learning (ML) algorithms is used to develop a fully unsupervised image quality assessment and clustering system. Our pipeline consists of a data pre-processing step where individual image objects are identified in a large astronomical image and converted to smaller pixel images. This data is then fed to a deep convolutional autoencoder jointly trained with a self-organizing map (SOM). This part can be used as a recommendation system. The resulting output is eventually mapped onto a two-dimensional grid using a second, deep, SOM. We use data taken from ground-based telescopes and, as a case study, compare the system's ability and performance with the results obtained by supervised methods presented by Teimoorinia et al. (2020). The availability of target labels in this data allowed a comprehensive performance comparison between our unsupervised and supervised methods. In addition to image-quality assessments performed in this project, our method can have various other applications. For example, it can help experts label images in a considerably shorter time with minimum human intervention. It can also be used as a content-based recommendation system capable of filtering images based on the desired content.