  Visual arts are of inestimable importance for the cultural, historic and
economic growth of our society. One of the building blocks of most analysis in
visual arts is to find similarity relationships among paintings of different
artists and painting schools. To help art historians better understand visual
arts, this paper presents a framework for visual link retrieval and knowledge
discovery in digital painting datasets. Visual link retrieval is accomplished
by using a deep convolutional neural network to perform feature extraction and
a fully unsupervised nearest neighbor mechanism to retrieve links among
digitized paintings. Historical knowledge discovery is achieved by performing a
graph analysis that makes it possible to study influences among artists. An
experimental evaluation on a database collecting paintings by very popular
artists shows the effectiveness of the method. The unsupervised strategy makes
the method interesting especially in cases where metadata are scarce,
unavailable or difficult to collect.
