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In this paper, we deal with the problem of automatic tag recommendation forpainting artworks. Diachronic descriptions containing deviations on thevocabulary used to describe each painting usually occur when the work is doneby many experts over time. The objective of this work is to provide a frameworkthat produces a more accurate and homogeneous set of tags for each painting ina large collection. To validate our method we build a model based on aweakly-supervised neural network for over $5{,}300$ paintings with hand-labeleddescriptions made by experts for the paintings of the Brazilian painter CandidoPortinari. This work takes place with the Portinari Project which started in1979 intending to recover and catalog the paintings of the Brazilian painter.The Portinari paintings at that time were in private collections and museumsspread around the world and thus inaccessible to the public. The descriptionsof each painting were made by a large number of collaborators over 40 years asthe paintings were recovered and these diachronic descriptions causeddeviations on the vocabulary used to describe each painting. Our proposedframework consists of (i) a neural network that receives as input the image ofeach painting and uses frequent itemsets as possible tags, and (ii) aclustering step in which we group related tags based on the output of thepre-trained classifiers.

Automatic Tag Recommendation for Painting Artworks Using Diachronic Descriptions