DOI QR코드

DOI QR Code

Hand-crafted 특징 및 머신 러닝 기반의 은하 이미지 분류 기법 개발

Development of Galaxy Image Classification Based on Hand-crafted Features and Machine Learning

  • 투고 : 2020.11.30
  • 심사 : 2021.01.29
  • 발행 : 2021.02.28

초록

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.

키워드

과제정보

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. 2020R1C1C1007423).

참고문헌

  1. G.I. Redfern, "Telescopes," Astrophotography is Easy!, Springer, Cham, pp. 55-76, 2020.
  2. D. Rouan, L. Tasca, G. Soucail, O. Le Fevre, "A Robust Morphological Classification of High-redshift Galaxies Using Support Vector Machines on Seeing Limited Images-I. Method Description," Astronomy & Astrophysics, Vol. 478, No. 3, pp. 971-980, 2008. https://doi.org/10.1051/0004-6361:20078625
  3. M. Banerji, O. Lahav, C.J. Lintott, F.B. Abdalla, K. Schawinski, S. P. Bamford, D. Andreescu, P. Murray, M. J. Raddick, A. Slosar, A. Szalay, D. Thomas, J. Vandenberg, "Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning," Monthly Notices of the Royal Astronomical Society, Vol. 406, No. 1, pp. 342-353, 2010. https://doi.org/10.1111/j.1365-2966.2010.16713.x
  4. X.P. Zhu, J.M. Dai, C.J. Bian, Y. Chen, S. Chen, C. Ho, "Galaxy Morphology Classification with Deep Convolutional Neural Networks," Astrophysics and Space Science, Vol. 364, No. 4, pp. 55, 2019. https://doi.org/10.1007/s10509-019-3540-1
  5. M. Jimenez, M.T. Torres, R. John, I. Triguero, "Galaxy Image Classification Based on Citizen Science Data: A Comparative Study," IEEE Access, Vol. 8, pp. 47232-47246, 2020. https://doi.org/10.1109/ACCESS.2020.2978804
  6. I. Selim, A.E. Keshk, B.M. El Shourbugy, "Galaxy Image Classification Using Non-negative Matrix Factorization," Int. J. Comput. Appl, Vol. 137, No. 5, pp. 4-8, 2016.
  7. D. Misra, S.N. Mohanty, M. Agarwal, S.K. Gupta, "Convoluted Cosmos: Classifying Galaxy Images Using Deep Learning," Data Management, Analytics and Innovation. Springer, Singapore, pp. 569-579 2020.
  8. J. De La Calleja, O. Fuentes, "Machine Learning and Image Analysis for Morphological Galaxy Classification," Monthly Notices of the Royal Astronomical Society, Vol. 349, No. 1, pp. 87-93, 2004. https://doi.org/10.1111/j.1365-2966.2004.07442.x
  9. S. Kasivajhula, N. Raghavan, H. Shah, "Morphological Galaxy Classification Using Machine Learning," Monthly Notices of the Royal Astronomical Society, Vol. 8, pp. 1-8, 2007.
  10. E.J. Kim, R.J. Brunner, "Star-galaxy Classification Using Deep Convolutional Neural Networks," Monthly Notices of the Royal Astronomical Society, pp. stw2672, 2016.
  11. N.E.M. Khalifa, M.H.N. Taha, A.E. Hassanien, I. M. Selim, "Deep Galaxy: Classification of Galaxies Based on Deep Convolutional Neural Networks," arXiv preprint, Vol. 1709, No. 02245, 2017.
  12. X.P. Zhu, J.M. Dai, C.J. Bian, Y. Chen, S. Chen, C. Hu, "Galaxy Morphology Classification with Deep Convolutional Neural Networks," Astrophysics and Space Science, Vol. 364, No. 4, pp. 55, 2019. https://doi.org/10.1007/s10509-019-3540-1
  13. C.R. Gonzalez, R.E. Woods, S.L. Eddins, "Digital Image Processing Using MATLAB," Pearson Education India, 2004.
  14. K. Zuiderveld, "Contrast Limited Adaptive Histogram Equalization," Graphics gems, pp. 474-485, 1994.
  15. I. Sobel"History and Definition of the Sobel Operator," Retrieved from the World Wide Web, Vol. 1505, 2014.
  16. E. Rublee, V. Rabaud, K. Konolige, "ORB: An Efficient Alternative to SIFT or SURF," 2011 International conference on computer vision. Ieee, 2011.
  17. E. Rosten, T. Drummond, "Machine Learning for High-speed Corner Detection," European conference on computer vision. Springer, Berlin, Heidelberg, 2006.
  18. M. Calonder, V. Lepetit, C. Strecha, P. Fua, "Brief: Binary Robust Independent Elementary Features," European conference on computer vision, Springer, Berlin, Heidelberg, 2010.
  19. F. Pedregosa, G. Varoquaux, A. Gramfor, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, "Machine Learning in Python," JMLR, Vol. 12, pp. 2825-2830, 2011
  20. H. Abdi, L.J. Williams, "Principal Component Analysis," Wiley interdisciplinary reviews: computational statistics, Vol. 2, No. 4, pp. 433-459, 2010. https://doi.org/10.1002/wics.101
  21. F.T. Liu, K.M. Ting, Z.H. Zhou, "Isolation Forest," 2008 Eighth IEEE International Conference on Data Mining, IEEE, 2008.
  22. L. Breiman, "Random Forests," Machine learning, Vol. 45, No. 1, pp. 5-32, 2001. https://doi.org/10.1023/A:1010933404324
  23. D.R. Cox, "The Regression Analysis of Binary Sequences," Journal of the Royal Statistical Society: Series B (Methodological), Vol. 20, No. 2, pp. 215-232, 1958. https://doi.org/10.1111/j.2517-6161.1958.tb00292.x
  24. C. Cortes, V. Vapnik, "Support-vector Networks," Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995. https://doi.org/10.1007/BF00994018
  25. Y. Freund, R.E. Schapire, "A Decision-theoretic Generalization of On-line Learning and an Application to Boosting," Journal of computer and system sciences, Vol. 55, No. 1, pp. 119-139, 1997. https://doi.org/10.1006/jcss.1997.1504
  26. P. Geurts, D. Ernst, L. Wehenkell, "Extremely Randomized Trees," Machine Learning, Vol. 63, No. 1, pp. 3-42, 2006. https://doi.org/10.1007/s10994-006-6226-1
  27. N.S. Altman, "An introduction to kernel and nearest-neighbor nonparametric regression," The American Statistician, Vol. 46, No. 3, pp. 175-185, 1992. https://doi.org/10.2307/2685209
  28. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, "Surf: Speeded up Robust Features," European conference on computer vision, Springer, Berlin, Heidelberg, 2006.
  29. D.G. Lowe, "Distinctive Image Features from Scale-invariant Keypoints," International journal of computer vision, Vol. 60, No. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  30. https://astronn.readthedocs.io/en/latest/galaxy10.html
  31. S. Han, J. Pool, J. Tran, W.J. Dally, "Learning both Weights and Connections for Efficient Neural Network," Advances in neural information processing systems 28, pp. 1135-1143, 2015.