期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 399, 期 3, 页码 1367-1372出版社
OXFORD UNIV PRESS
DOI: 10.1111/j.1365-2966.2009.15366.x
关键词
methods: data analysis; techniques: image processing
资金
- Intramural Research Program of the NIH
- National Institute on Aging
- Alfred P. Sloan Foundation
- National Science Foundation
- US Department of Energy
- National Aeronautics and Space Administration
- Japanese Monbukagakusho
- Max Planck Society
- Higher Education Funding Council for England
We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using manually classified images of elliptical, spiral and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbour rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with an accuracy of similar to 90 per cent compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects.
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