Morphology-based Query for Galaxy Image Databases

Abstract

Galaxies of rare morphology are of paramount scientific interest, as they carry important information about the past, present, and future universe. Once a rare galaxy is identified, studying it more effectively requires a set of galaxies of similar morphology, allowing generalization and statistical analysis that cannot be done when N = 1. Databases generated by digital sky surveys can contain a very large number of galaxy images, and therefore once a rare galaxy of interest is identified it is possible that more instances of the same morphology are also present in the database. However, when a researcher identifies a certain galaxy of rare morphology in the database, it is virtually impossible to mine the database manually in the search for galaxies of similar morphology. Here we propose a computer method that can automatically search databases of galaxy images and identify galaxies that are morphologically similar to a certain user-defined query galaxy. That is, the researcher provides an image of a galaxy of interest, and the pattern recognition system automatically returns a list of galaxies that are visually similar to the target galaxy. The algorithm uses a comprehensive set of descriptors, allowing it to support different types of galaxies, and it is not limited to a finite set of known morphologies. While the list of returned galaxies is neither clean nor complete, it contains a far higher frequency of galaxies of the morphology of interest, providing a substantial reduction of the data. Such algorithms can be integrated into data management systems of autonomous digital sky surveys such as the Large Synoptic Survey Telescope (LSST), where the number of galaxies in the database is extremely large. The source code of the method is available at

A Catalog of Automatically Detected Ring Galaxy Candidates in PanSTARSS

“…Due to the large sizes of these databases, effective identification of these objects requires automation, leading to the development of automatic methods of identifying peculiar objects in large databases of galaxy images (Shamir 2012(Shamir , 2016Shamir & Wallin 2014). Here we describe an automatic image analysis method that can identify ring galaxies and apply the method to mine through ∼3×10 6 galaxies imaged by the Panoramic Survey Telescope and Rapid Response System (Hodapp et al 2004;Chambers et al 2016;Flewelling et al 2016) to compile a catalog of ring galaxy candidates.…”

Detection of unknown galaxy types in large databases of galaxy images

“…In summary, the WND-CHARM library computes a comprehensive set of 2883 numerical image content descriptors that reflect numerous aspects of the visual content such as the shape, color, edges, textures (e.g., Gabor, Haralick, Tamura), fractals, polynomial decomposition of the image (e.g., Chebyshev polynomials, Zernike), and statistics of the distribution of the pixel values (e.g., Radon features, multi-scale histograms, first four moments). That feature set is described in detail in [14,19,20], and is applied successfully to the task of galaxy image analysis [21,22].…”