Semantic hashing for fast solar magnetogram retrieval

• Autorzy: Grycuk Rafał , Scherer Rafał , Marchlewska Alina , Napoli Christian

Identyfikatory

DOI

10.2478/jaiscr-2022-0020

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

content-based image retrieval; image descriptor; solar analysis

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2022
• Tom: Vol. 12, No. 4
• Strony: 299--306
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Grycuk Rafał

• Department of Intelligent Computer Systems, Częstochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Scherer Rafał

• Department of Intelligent Computer Systems, Częstochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Marchlewska Alina

• Institute of Information Technologies, University of Social Sciences ul. Sienkiewicza 9, 90-113 Łodź, Poland

autor

Napoli Christian

• Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Roma 00185, Italy

Bibliografia

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• [3] Stuart Mumford, Nabil Freij et al., Sunpy: A python package for solar physics, Journal of Open Source Software, vol. 5, no. 46, p. 1832, 2020. [Online]. Available: https://doi.org/10.21105/joss.01832
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