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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.1
- 수록면
- 17 - 27 (11page)
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In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.
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참고문헌 (30)
참고문헌 신청
G. I. Redfern / 2020 / Astrophotography is Easy! / Springer :55~76
D. Rouan / 2008 / A Robust Morphological Classification of High-redshift Galaxies Using Support Vector Machines on Seeing Limited Images-I. Method Description / Astronomy &Astrophysics 478(3):971~980
M. Banerji / 2010 / Galaxy Zoo : Reproducing Galaxy Morphologies Via Machine Learning / Monthly Notices of the Royal Astronomical Society 406(1):342~353
M. Jiménez / 2020 / Galaxy Image Classification Based on Citizen Science Data : A Comparative Study / IEEE Access 8:47232~47246
I. Selim / 2016 / Galaxy Image Classification Using Non-negative Matrix Factorization / Int. J. Comput. Appl 137(5):4~8
D. Rouan / 2008 / A Robust Morphological Classification of High-redshift Galaxies Using Support Vector Machines on Seeing Limited Images-I. Method Description / Astronomy &Astrophysics 478(3):971~980
M. Banerji / 2010 / Galaxy Zoo : Reproducing Galaxy Morphologies Via Machine Learning / Monthly Notices of the Royal Astronomical Society 406(1):342~353
M. Jiménez / 2020 / Galaxy Image Classification Based on Citizen Science Data : A Comparative Study / IEEE Access 8:47232~47246
I. Selim / 2016 / Galaxy Image Classification Using Non-negative Matrix Factorization / Int. J. Comput. Appl 137(5):4~8
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