论文标题
使用历时描述为绘画艺术品的自动标签建议
Automatic Tag Recommendation for Painting Artworks Using Diachronic Descriptions
论文作者
论文摘要
在本文中，我们处理绘画艺术品的自动标签建议问题。含有用于描述每幅画的词汇偏差的历时描述通常是在许多专家随着时间的流逝完成的。这项工作的目的是提供一个框架，该框架为大型收藏中的每幅画提供了更准确和均匀的标签。为了验证我们的方法，我们建立了一个基于弱监督的神经网络的模型，价格超过5美元{，} 300 $绘画，专家为巴西画家Candido Portinari的绘画制作了手工标记的描述。这项工作是在1979年开始的Portinari项目中进行的，该项目旨在恢复和分类巴西画家的绘画。当时的Portinari绘画是在私人收藏中，博物馆遍布世界各地，因此公众无法访问。每幅画的描述是由40年来的40年来进行的，随着绘画的恢复，这些历时性描述引起了用于描述每幅画的词汇的偏差。我们提出的框架由（i）一个神经网络组成，该神经网络以输入每幅画的图像，并使用频繁的项目集输入，以及（ii）基于预训练的分类器的输出，我们将相关标签分组的聚类步骤。
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.
