文章基本信息

标题：An Astronomical Image Content-based Recommendation System Using Combined Deep Learning Models in a Fully Unsupervised Mode
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作者：Hossen Teimoorinia ; Sara Shishehchi ; Ahnaf Tazwar 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2021
卷号：161
期号：5
页码：1-21
DOI：10.3847/1538-3881/abea7e
语种：English
出版社：American Institute of Physics
摘要：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 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 auto-encoder 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. The availability of target labels in this data allowed for 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.