星系图像的自动形态分类。
Automatic morphological classification of galaxy images.
作者信息
Shamir Lior
机构信息
Laboratory of Genetics, NIA/NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA.
出版信息
Mon Not R Astron Soc. 2009 Nov 1;399(3):1367-1372. doi: 10.1111/j.1365-2966.2009.15366.x.
We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a 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 Neighbor 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 accuracy of ~90% 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.
我们描述了一种图像分析监督学习算法，它可以自动对星系图像进行分类。该算法首先使用椭圆星系、螺旋星系和侧向星系的手动分类图像进行训练。从每个图像中提取大量图像特征，并使用Fisher分数选择最具信息性的特征。然后可以使用简单的加权最近邻规则对测试图像进行分类，将Fisher分数用作特征权重。实验结果表明，与作者进行的分类相比，来自星系动物园的星系图像可以自动分类为螺旋星系、椭圆星系和侧向星系，准确率约为90%。该算法的完整可编译源代码可免费下载，其通用性使其适用于其他涉及天体自动图像分析的用途。
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