A computer vision approach to mining big solar data

Author

Felix, Sarah ; Csillaghy, Andre

Author_Institution

Univ. of Appl. Sci. Northwestern Switzerland, Windisch, Switzerland

fYear

2014

fDate

27-30 Oct. 2014

Firstpage

27

Lastpage

35

Abstract

Extracting and indexing relevant information with computer vision algorithms in very large solar image archives allows investigating solar activity from a new perspective. Using computer vision algorithms, we have developed methods that work with very compact and concise descriptions of images. We apply our method to images from the Solar Dynamics Observatory (SDO) and present a proof-of-concept Query by Example (QBE) system. In addition we introduce a benchmark dataset, on one hand to evaluate our system, and on the other hand to allow comparisons of our results with other QBE systems in this domain.

Keywords

Big Data; astronomy computing; computer vision; data mining; visual databases; Solar Dynamics Observatory; big solar data mining; computer vision algorithms; information extraction; information indexing; query by example system; solar activity; very large solar image archives; Computer vision; Data mining; Feature extraction; Heuristic algorithms; Transforms; Visualization; Vocabulary; Computer Vision; Query by Example (QBE); Solar Dynamics Observatory (SDO); benchmarking;

fLanguage

English

Publisher

ieee

Conference_Titel

Big Data (Big Data), 2014 IEEE International Conference on

Conference_Location

Washington, DC

Type

conf

DOI

10.1109/BigData.2014.7004401

Filename

7004401