用于快速太阳磁图检索的语义哈希

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rafał Grycuk, R. Scherer, A. Marchlewska, Christian Napoli
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引用次数: 2

摘要

摘要我们提出了一种基于内容的太阳磁图检索方法。我们使用SunPyPyTorch库收集的SDO太阳地震和磁成像仪输出。我们以矢量的形式创建了太阳磁场区域的数学表示。多亏了这个解决方案,我们可以比较短矢量,而不是比较全磁盘图像。为了减少检索时间,我们使用了一个完全连接的自动编码器,它将256元素的描述符简化为32元素的语义哈希。实验和比较证明了该方法的有效性。与其他最先进的方法相比,我们的方法具有最高的精度值。所提出的方法不仅可以用于太阳图像检索,还可以用于分类任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Hashing for Fast Solar Magnetogram Retrieval
Abstract We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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