  Considering the huge amount of art pieces that exist, there is valuable
information to be discovered. Examining a painting, an expert can determine its
style, genre, and the time period that the painting belongs. One important task
for art historians is to find influences and connections between artists. Is
influence a task that a computer can measure? The contribution of this paper is
in exploring the problem of computer-automated suggestion of influences between
artists, a problem that was not addressed before in a general setting. We first
present a comparative study of different classification methodologies for the
task of fine-art style classification. A two-level comparative study is
performed for this classification problem. The first level reviews the
performance of discriminative vs. generative models, while the second level
touches the features aspect of the paintings and compares semantic-level
features vs. low-level and intermediate-level features present in the painting.
Then, we investigate the question "Who influenced this artist?" by looking at
his masterpieces and comparing them to others. We pose this interesting
question as a knowledge discovery problem. For this purpose, we investigated
several painting-similarity and artist-similarity measures. As a result, we
provide a visualization of artists (Map of Artists) based on the similarity
between their works
