  In this paper, we deal with the problem of automatic tag recommendation for
painting artworks. Diachronic descriptions containing deviations on the
vocabulary used to describe each painting usually occur when the work is done
by many experts over time. The objective of this work is to provide a framework
that produces a more accurate and homogeneous set of tags for each painting in
a large collection. To validate our method we build a model based on a
weakly-supervised neural network for over $5{,}300$ paintings with hand-labeled
descriptions made by experts for the paintings of the Brazilian painter Candido
Portinari. This work takes place with the Portinari Project which started in
1979 intending to recover and catalog the paintings of the Brazilian painter.
The Portinari paintings at that time were in private collections and museums
spread around the world and thus inaccessible to the public. The descriptions
of each painting were made by a large number of collaborators over 40 years as
the paintings were recovered and these diachronic descriptions caused
deviations on the vocabulary used to describe each painting. Our proposed
framework consists of (i) a neural network that receives as input the image of
each painting and uses frequent itemsets as possible tags, and (ii) a
clustering step in which we group related tags based on the output of the
pre-trained classifiers.
