  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 morphology. 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 http://vfacstaff.ltu.edu/lshamir/downloads/udat.
