2009/08/26 by Lior Shamir · 2 citations
Computer Science · Physics and Astronomy · #Advanced Vision and Imaging #Artificial intelligence #Astrophysics #Computer science #Elliptical galaxy #Feature (linguistics) #Galaxy #Gaussian Processes and Bayesian Inference #Image (mathematics) #Image Retrieval and Classification Techniques #Interacting galaxy #Irregular galaxy #Pattern recognition (psychology) #Physics #Set (abstract data type) #Spiral galaxy #Unbarred spiral galaxy #astro-ph.GA #astro-ph.IM
paper · pdf · doi:10.1111/j.1365-2966.2009.15366.x
Accepted for publication in MNRAS
arxiv created 2009/08/26 · openalex publication_date 2009/09/24 · arxiv updated 2015/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.