  Modern astronomy relies on massive databases collected by robotic telescopes
and digital sky surveys, acquiring data in a much faster pace than what manual
analysis can support. Among other data, these sky surveys collect information
about millions and sometimes billions of extra-galactic objects. Since the very
large number of objects makes manual observation impractical, automatic methods
that can analyze and annotate extra-galactic objects are required to fully
utilize the discovery power of these databases. Machine learning methods for
annotation of celestial objects can be separated broadly into methods that use
the photometric information collected by digital sky surveys, and methods that
analyze the image of the object. Here we describe a hybrid method that combines
photometry and image data to annotate galaxies by their morphology, and a
method that uses that information to identify objects that are visually similar
to a query object (query-by-example). The results are compared to using just
photometric information from SDSS, and to using just the morphological
descriptors extracted directly from the images. The comparison shows that for
automatic classification the image data provide marginal addition to the
information provided by the photometry data. For query-by-example, however, the
analysis of the image data provides more information that improves the
automatic detection substantially. The source code and binaries of the method
can be downloaded through the Astrophysics Source Code Library.
