In the past few years, the number of fine-art collections that are digitized
and publicly available has been growing rapidly. With the availability of such
large collections of digitized artworks comes the need to develop multimedia
systems to archive and retrieve this pool of data. Measuring the visual
similarity between artistic items is an essential step for such multimedia
systems, which can benefit more high-level multimedia tasks. In order to model
this similarity between paintings, we should extract the appropriate visual
features for paintings and find out the best approach to learn the similarity
metric based on these features. We investigate a comprehensive list of visual
features and metric learning approaches to learn an optimized similarity
measure between paintings. We develop a machine that is able to make
aesthetic-related semantic-level judgments, such as predicting a painting's
style, genre, and artist, as well as providing similarity measures optimized
based on the knowledge available in the domain of art historical
interpretation. Our experiments show the value of using this similarity measure
for the aforementioned prediction tasks.

本文研究了如何提取适当的视觉特征，找到学习这些特征的最佳方法，进而学习绘画之间的相似度，探讨了构建多媒体系统来存档和检索艺术品数字化收藏的相关问题，并提出用于美学上的语义层预测，如预测绘画风格，流派和艺术家，实验结果表明该相似度测量方法适用于上述预测任务。