This lecture at the Intelligent Systems and Data mining workshop at faculty of Computers and Information, Kafer Elshikh University, 6 Dec. 2017

1. Kafr ElSheikh Workshop – 6/12/2017
DEEP GALAXY: CLASSIFICATION OF GALAXIES BASED ON DEEP
CONVOLUTIONAL NEURAL NETWORKS
Nour Eldeen Mahmoud Khalifa
Lecturer of Information Technology – FCI, Cairo University
SRGE Member
SRGE 2017
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Dr. Mohamed Hamed N. Taha Prof. Aboul Ella Hassanien Prof. I. M. Selim

2. Agenda
 Introduction
 Problem Statement
 Proposed Approach
 Experimental Results
 Publication
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3. Introduction
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Galaxy
A system of millions or billions of stars, together
with gas and dust, held together by gravitational
attraction.
Kafr ElSheikh University Workshop – 6/12/2017

4. Introduction
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How Many Galaxies
100 billion and 200 billion galaxies in the observable
Universe that human-made technology can detect.
Astronomers at the University of Nottingham now say the number of galaxies in the observable
Universe is 2 trillion, more than 10 times as many as previously thought.
Kafr ElSheikh University Workshop – 6/12/2017

5. 5
Workshop on Swarm Intelligence and Application (SIA), FCI, CU - Sat 23-09-2017

6. Introduction
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 How to observe
Kafr ElSheikh University Workshop – 6/12/2017

7. Introduction
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 Types of Galaxies
Elliptical, Spiral, and Irregular
Kafr ElSheikh University Workshop – 6/12/2017

8. Introduction
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 Types of Galaxies
 Hubble Classification
Kafr ElSheikh University Workshop – 6/12/2017

9. Problem Statement
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 Enable Computer to classify the types of galaxies
automatically.
 Why !!
 Education Purposes.
 Classifying billions of galaxies. (Trillions in near future)
Kafr ElSheikh University Workshop – 6/12/2017

10. Proposed Approach
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Year Publication Accuracy
2004 Machine learning and image analysis for morphological galaxy
classification
91%
2007 A Hierarchical Model for Morphological Galaxy Classification 91.64%
2017 Galaxy Image Classification using Non-Negative Matrix
Factorization
93%
Kafr ElSheikh University Workshop – 6/12/2017

11. Proposed Approach
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 Deep Learning “Convolutional Neural Networks”
 Second Appearance.
 The Rise of Deep Learning.
 Why now !!
 More Powerful Gpu’s and Cpu’s
 Large Datasets.
Kafr ElSheikh University Workshop – 6/12/2017

12. Proposed Approach
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Workshop on Swarm Intelligence and Application (SIA), FCI, CU - Sat 23-09-2017

13. Proposed Approach
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Workshop on Swarm Intelligence and Application (SIA), FCI, CU - Sat 23-09-2017

14. Proposed Approach
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Kafr ElSheikh University Workshop – 6/12/2017

15. Proposed Approach
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Kafr ElSheikh University Workshop – 6/12/2017

16. Experimental Results
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 Dataset
This catalog dataset consists of more than 11,000
images and contains samples from different Hubble
types galaxies.
Trained over 1346 image only.
Kafr ElSheikh University Workshop – 6/12/2017

17. Experimental Results
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 Implemented using a software package (MATLAB).
 The implementation was CPU specific. All
experiments were conducted on a server with Intel
Xeon E5-2620 processor (2 GHz) and 96 GB Ram.
Kafr ElSheikh University Workshop – 6/12/2017

18. Experimental Results
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Typical first convolutional and RELU layer
features visualization.
Kafr ElSheikh University Workshop – 6/12/2017

19. Experimental Results
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Typical first fully connected layer
visualization.
Kafr ElSheikh University Workshop – 6/12/2017

20. Experimental Results
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Training accuracy progress against a number
of iteration in the training phase.
Kafr ElSheikh University Workshop – 6/12/2017

21. Experimental Results
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Year Publication Accuracy
2004 Machine learning and image analysis for morphological galaxy
classification
91%
2007 A Hierarchical Model for Morphological Galaxy Classification 91.64%
2017 Galaxy Image Classification using Non-Negative Matrix
Factorization
93%
2017 Deep Galaxy: Classification of Galaxies based on Deep
Convolutional Neural Networks
97.272%
Kafr ElSheikh University Workshop – 6/12/2017

22. Publication
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Kafr ElSheikh University Workshop – 6/12/2017

23. Publication
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Kafr ElSheikh University Workshop – 6/12/2017

24. Thank you
nourmahmoud@cu.edu.eg
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Kafr ElSheikh University Workshop – 6/12/2017